Enhancing mobile EEG: Software development and performance insights of the DreamMachine

Ha dwa e A icle Enhancing mobile EEG: So wa e de elopmen and pe o mance insigh s o he D eamMachine Pa ia Samimisabe * , Lau a K iege * , Ma c Vidal De Palol, Deniz Gün , Go don Pipa Ins i u e o Cogni i e Science, Osnab ueck Uni e si y, 49074 Osnab ueck, Ge many ARTICLE INFO Keywo ds: D eamMachine EEGD oid Mobile EEG Elec oencephalog aphy (EEG) And oid applica ion Eyes open/closed ABSTRACT Elec oencephalog aphy (EEG) is widely used in ields such as neu ology, cogni i e neu oscience, sleep esea ch, and men al heal h. I eco ds b ain elec ical ac i i y o s udy neu ophysiological unc ions. Nume ous EEG and mobile EEG sys ems a e a ailable. Howe e , adhe ence o he s anda ds se by he In e na ional Fede a ion o Clinical Neu ophysiology (IFCN) is essen ial o ensu ing high-quali y da a collec ion in clinical en i onmen s. The D eamMachine, a mobile EEG de ice, complies ully wi h hese s anda ds, o e ing 24-channel eco dings a 250 Hz, Blue oo h Low Ene gy (BLE), and capabili ies o elec ooculog aphy (EOG) and elec oca diog aphy (ECG). I s low cos makes i an accessible op ion o EEG s udies. The so wa e a chi ec u e o he open- sou ce D eamMachine is de ailed in his s udy. Focus is placed on da a comp ession and communica ion be ween he de ice and i s companion And oid applica ion. The de ails o he And oid applica ion’s ea u es, including gain se ings, bi s pe channel, il e s, bi -shi ing, and sa e y ac o s, a e in es iga ed. Subsequen ly, he sys em’s pe o mance is e alua ed h ough a s anda d eyes-open/eyes-closed expe imen , compa ing i s esul s wi h a labo a o y EEG sys em ac oss a signi ican numbe o pa icipan s o assess he pe o mance o he D eamMachine sys em. Speci ica ions able Ha dwa e name D eamMachine Subjec a ea •Neu oscience •Educa ional ools and open-sou ce al e na i es o exis ing in as uc u e •Gene al Ha dwa e ype •Measu ing physical p ope ies and in-lab senso s •Elec ical enginee ing and compu e science •Neu oscience ools •O he : Mobile EEG de ice Closes comme cial analog OpenBCI, SMARTING mobi (con inued on nex page) * Co esponding au ho . E-mail add esses: [email p o ec ed] (P. Samimisabe ), [email p o ec ed] (L. K iege ), [email p o ec ed] (M.V. De Palol), [email p o ec ed] (D. Gün), [email p o ec ed] (G. Pipa). Con en s lis s a ailable a ScienceDi ec Ha dwa eX jou nal homepage: www.else ie .com/loca e/ohx h ps://doi.o g/10.1016/j.ohx.2025.e00689 Recei ed 19 Janua y 2025; Recei ed in e ised o m 11 Augus 2025; Accep ed 13 Augus 2025 Ha dwa eX 23 (2025) e00689 A ailable online 19 Augus 2025 2468-0672/© 2025 The Au ho (s). Published by Else ie L d. This is an open access a icle unde he CC BY license ( h p://c ea i ecommons.o g/licenses/by/4.0/ ). (con inued) Ha dwa e name D eamMachine Open-sou ce license GNU Gene al Public License 3.0 Cos o ha dwa e A ound 150 Eu o Sou ce ile eposi o y h ps://doi.o g/10.5281/zenodo.14627406 1. Ha dwa e in con ex EEG is one o he mos non-in asi e me hods o eco ding b ain ac i i y [1]. A po able e sion, known as mobile EEG, allows o i s use ou side o adi ional labo a o y se ings [2]. EEG sys ems ha e a wide ange o applica ions, and hei e sa ili y has been explo ed ac oss a ious domains [3]. Thei signi icance is e iden in bo h scien i ic esea ch and p ac ical applica ions. Fo ins ance, EEG is equen ly employed in cogni i e neu oscience, whe e b ainwa e pa e ns a e analyzed o e eal he neu al mechanisms un- de lying cogni i e p ocesses such as a en ion, memo y, pe cep ion, and language, o e ing aluable insigh s in o he complexi ies o human cogni ion [4–6]. In clinical diagnosis and moni o ing, EEG assumes a pi o al ole in diagnosing and moni o ing neu ological diso de s like epilepsy, sleep diso de s, and b ain inju ies by eco ding abno mal elec ical ac i i ies o acili a e p ecise diagnoses and ailo ed ea men plans, e en assis ing in pinpoin ing abno mal b ain ac i i y loca ions o su gical planning in epilepsy cases [7–9]. In neu o eedback he apy, EEG-based echniques a e employed in he apeu ic se ings o enable indi iduals o egula e b ain ac i i y pa e ns h ough eal- ime eedback, o e ing signi ican bene i s o condi ions such as a en ion de ici hype ac i i y diso de (ADHD) and anxie y diso de s [10,11]. Fu he mo e, EEG plays a pi o al ole in de eloping b ain compu e in e aces (BCI), enabling in- di iduals wi h se e e mo o disabili ies o con ol ex e nal de ices using b ain signals, anging om compu e in e ac ions o imme si e i ual eali y expe iences [12,13]. EEG also enhances emo ional and mood analysis by e ealing how he b ain p ocesses emo ions in esponse o s imuli, con ibu ing aluable insigh s o a ec i e compu ing and psychological esea ch by unco e ing pa e ns o emo ional s a es [14]. Addi ionally, EEG is u ilized in educa ional esea ch o s udy lea ning p ocesses and in e en ions, allowing esea che s o assess he impac o eaching me hods on b ain ac i i y and cogni i e engagemen , ul ima ely e ining ins uc ional s a egies [15,16]. In psychological s udies, EEG aids in in es iga ing b ain p ocesses ela ed o decision-making, p oblem-sol ing, and social in e ac ions, p o iding aluable insigh s in o he neu al ounda ions o human beha io and cogni ion [17–20]. No ably, many o hese applica ions can yield meaning ul conclusions wi hou equi ing high esolu ion o many elec odes. This end is accele a ed by he ise o deep lea ning and o he machine lea ning app oaches, which enhance signal p ocessing and enable eliable conclusions om smalle da ase s [21]. Al hough hese ools and algo i hms can compensa e o weaknesses in he o iginal da a, imp o ing sys ems o eco ding high-quali y da a emains c ucial. A wide ange o EEG and mobile EEG sys ems wi h a ious con igu a ions a e a ailable on he ma ke [22–24]. Howe e , he IFCN has es ablished s anda ds o EEG de ices o ensu e high-quali y and consis en da a eco ding in clinical se ings [25]. These spec- i ica ions a e designed o main ain he accu acy, eliabili y, and usabili y o EEG eco dings. They include a minimum o 24 channels, a sampling a e o a leas 200 Hz, a ange o il e s, a esolu ion o no less han 12-bi , and he p o ision o da a in a s anda d ile o ma . The D eamMachine ully mee s all hese equi emen s. This de ice connec s o a companion app on an And oid de ice ia Blue oo h echnology. I ea u es a ligh weigh design, o e s a ba e y li e o up o six hou s, and is capable o eco ding 24 channels a a 250 Hz sampling a e [16]. Addi ionally, his de ice can be easily adjus ed o cap u e EOG and ECG signals i needed. Rema kably, he Fig. 1. Illus a ion o he D eamMachine ha dwa e sys em. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 2 p oduc ion cos o he de ice emains below 150 Eu os (as o he design pe iod) [16]. Mo eo e , e e y aspec o he p ojec , om a de ailed explana ion o he ha dwa e con igu a ion o he sou ce code o he companion app, is openly accessible and a ailable o un es ic ed use and on Gi Hub eposi o y. Following he discussion o ele an esea ch on mobile EEG de ices and simila p ojec s, he ocus shi s o he so wa e com- ponen s o he mobile EEG sys ems. In his s udy, he so wa e de ails o he D eamMachine p ojec a e in es iga ed. This includes an explana ion o da a comp ession and decomp ession echniques, es ablishing bidi ec ional communica ion be ween he companion applica ion and he EEG de ice, and he in oduc ion o he il e s used wi hin he applica ion. The analysis concludes wi h a pe - o mance e alua ion based on a s anda d eyes-open and eyes-closed expe imen , compa ing he D eamMachine de ice wi h a labo- a o y EEG sys em (asalab™ by ANT Neu o) [26]. Fo a de ailed discussion on he ha dwa e aspec s, please e e o [16]. 2. Ha dwa e desc ip ion The D eamMachine ha dwa e sys em is shown in Fig. 1. I is a compac , mobile EEG de ice ha s eams eal- ime b ain ac i i y da a o a dedica ed And oid applica ion (EEGD oid) ia BLE. The sys em uses a 24-bi esolu ion ADC o accu a ely eco d neu al signals, wi h con igu able op ions o ope a e a 10-, 14-, o 16-bi esolu ion depending on use needs. Use s can also selec be ween sampling a es o 250 Hz and 167 Hz. Physically, D eamMachine is a ec angula boa d measu ing 58.55 ×51.03 mm (L ×W), weighing 40 g wi h he ba e y and 22 g wi hou . The ba e y suppo s up o six hou s o con inuous eco ding and weighs app oxima ely 18 g. The sys em is equipped wi h 24 EEG channels, co esponding o s anda d 10–20 elec ode loca ions: FP1, FPZ, FP2, F7, F3, FZ, F4, F8, M1, T7, C3, CZ, C4, T8, M2, P7, P3, PZ, P4, P8, POZ, O1, OZ, and O2. I also includes wo addi ional channels, one o e e ence and one o g ound, o a o al o 26 inpu connec ions. Each o hese 26 channels is accessible ia solde ed pins on he PCB, enabling use s o connec a wide ange o elec ode ypes, including s ick-on, pads, cap-moun ed, we /gel-based, a oo, o lexible p in ed elec odes. This open connec o layou suppo s high lexibili y in expe imen al se ups, making he de ice sui able o di e se applica ions in esea ch, educa ion, and p o o yping. 2.1. Da a ansmission To ans e da a om he D eamMachine sys em o he And oid applica ion, he analog EEG signals mus i s be con e ed in o digi al o m using analog- o-digi al componen s. This con e sion is pe o med wi h 24-bi esolu ion, ensu ing high p ecision in ep esen ing he signals. The sys em samples da a a a a e o 250 Hz, cap u ing 250 da a poin s pe second. Howe e , ansmi ing aw 24-bi da a o each channel would equi e subs an ial bandwid h, making he p ocess ine icien and po en ially cos ly. To o e come his limi a ion, a da a comp ession echnique is applied be o e ansmission. This educes bandwid h usage while p ese ing signal quali y. Wi hou comp ession, ansmi ing he ull 24-bi da a would signi ican ly limi he de ice’s sampling a e and he numbe o channels i could suppo . Ins ead o sending ull 24-bi samples, he da a is comp essed o 16 bi s, which is mo e p ac ical o wi eless ansmission. While his educes some p ecision, i g ea ly imp o es e iciency. The comp ession me hod wo ks by ansmi ing he di e ence be ween he cu en sample and a p edic ed alue a he han he absolu e eco ded ol age. The p edic ion is ypically based on he p e ious sample o a weigh ed combina ion o ea lie samples, using a echnique known as di e ence coding, del a encoding, o Di e en ial Pulse Code Modula ion (DPCM) [39]. This app oach akes ad an age o he s ong co ela ion be ween successi e EEG samples o ep esen he signal mo e compac ly. Ano he c ucial aspec o he da a ansmission p ocess is Adap i e Di e en ial Pulse Code Modula ion (ADPCM) [40]. ADPCM ensu es ha only he mos ele an 16-bi pe channel a e ansmi ed ins ead o all 24-bi . By encoding he di e ence be ween consecu i e samples, ADPCM comp esses digi al signals, main aining high esolu ion and inco po a ing an au oma ic a i ac il e o enhance da a quali y. These app oaches allow he ansmission o 24 channels wi h a sampling a e o up o 250 Hz, p ese ing high esolu ion while minimizing he ansmi ed da a olume. Addi ionally, ADPCM helps il e ou a i ac s, ensu ing highe quali y ansmi ed da a. Howe e , some da a loss o e o s may occu du ing Blue oo h ansmission due o a ious ac o s. I is impo an o be awa e ha da a loss can occu du ing Blue oo h ansmission, as Blue oo h elies on adio wa es o da a ans e . Se e al ac o s inhe en o he na u e o wi eless communica ion can con ibu e o his issue. The physical dis ance be ween de ices, Blue oo h signal s eng h, and in e e ence om o he wi eless signals including Wi-Fi, mic owa es, o o he Blue oo h de- ices, can all deg ade he quali y o he connec ion. These ac o s can lead o an uns able Blue oo h connec ion, which may esul in los o co up ed da a packe s, causing e o s o incomple e da a on he ecei ing de ice. In addi ion o en i onmen al ac o s, de ice limi a ions can u he exace ba e ansmission issues. Fo ins ance, high CPU load on he ecei ing de ice may hinde i s abili y o p ocess incoming da a e icien ly, leading o da a loss. Fu he mo e, ou da ed Blue oo h ha dwa e o so wa e can make de ices incompa ible wi h he la es Blue oo h p o ocols, which may impac he s abili y and eliabili y o he connec ion. Se e al consid- e a ions should be made o minimize packe loss and a oid decoding e o s and signal shi ing du ing Blue oo h ansmission. To op imize Blue oo h ansmission and minimize da a loss, se e al s a egies should be conside ed. Fi s , i is essen ial o ensu e ha de ices a e placed wi hin an op imal ange, ypically 1–10 m, depending on he Blue oo h class, o main ain a s ong signal and educe packe loss. Addi ionally, posi ioning de ices in a eas wi h minimal physical obs uc ions, such as walls o me al objec s, is c ucial o p e en signal a enua ion. Nex , employing he la es Blue oo h e sions, such as Blue oo h 5.0 o la e , along wi h ad anced p o ocols like BLE, can signi ican ly imp o e ansmission eliabili y, ene gy e iciency, and esis ance o in e e ence. Fu he mo e, in eg a ing obus e o -co ec ion mechanisms, such as Fo wa d E o Co ec ion (FEC) o Au oma ic Repea eQues (ARQ), in o he Blue oo h p o ocol s ack can u he enhance da a in eg i y and educe packe loss [41]. Ano he impo an conside a ion is a oiding P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 3 Radio F equency (RF) in e e ence, as Blue oo h ope a es in he 2.4 GHz ISM band, which is sha ed wi h de ices like Wi-Fi ou e s and mic owa es. To mi iga e his in e e ence, using Blue oo h de ices ha suppo adap i e equency hopping (AFH), and dual-band suppo (2.4 GHz and 5 GHz) can imp o e connec ion s abili y [42]. Reducing he load on he ecei ing de ice is also i al; op i- mizing i s CPU pe o mance by closing unnecessa y applica ions o o loading asks o ex e nal ha dwa e can help main ain e icien da a p ocessing. Ensu ing ha bo h de ices ha e an adequa e powe supply is equally impo an , as low ba e y le els can lead o signal ins abili y and deg aded ansmission quali y. Finally, in en i onmen s wi h mul iple Blue oo h de ices, such as sma homes o IoT se ings, managing he Blue oo h ne wo k e icien ly, h ough segmen a ion o using Blue oo h mesh ne wo ks, can help p e en conges ion, he eby imp o ing communica ion eliabili y and s abili y o e la ge a eas. 2.2. Blue oo h Blue oo h is he mos widely adop ed wi eless communica ion me hod in mode n mobile EEG sys ems, including hose wi h ela i ely high channel coun s and sampling a es. I s popula i y s ems om se e al key ad an ages, including low powe con- sump ion, s able connec i i y, and a use - iendly pai ing p ocess, all o which make i well sui ed o eal- ime EEG applica ions. The D eamMachine le e ages BLE o da a ansmission, making i especially e ec i e in mobile scena ios such as sleep moni- o ing, whe e long ba e y li e and ene gy e iciency a e c i ical. To ope a e e ec i ely wi hin Blue oo h’s bandwid h limi a ions, he sys em employs a cus om comp ession algo i hm ha enables e icien ansmission o high- esolu ion EEG da a wi hou packe loss o deg ada ion in pe o mance. While some sys ems op o Wi-Fi o suppo ex emely high- h oughpu needs (e.g., uncomp essed, mul ichannel, high- equency eco dings), Blue oo h emains a eliable and p o en solu ion o mos mobile EEG applica ions. I ’s ypical ange (~2 m) is gene ally su icien when he ecei e de ice, such as a sma phone o able , emains nea he pa icipan . In con as o adi ional EEG sys ems ha ely on cabled connec ions o a compu e , which es ic mobili y and in oduce mo ion a i ac s om cable sway, Blue oo h-based wi eless ansmission o e s a signi ican imp o emen . As i s demons a ed by Debene e al [43], elimina ing cables enhances pa icipan mobili y and educes mo ion-induced noise, esul ing in a mo e p ac ical and use - iendly expe ience. Al oge he , Blue oo h, especially when combined wi h D eamMachine’s e icien comp ession s a egy, s ikes an e ec i e balance be ween e iciency, eliabili y, and usabili y, making i a highly sui able and op imized da a ansmission me hod o mode n mobile EEG sys ems. 2.3. And oid applica ion (EEGD oid) The EEGD oid applica ion, de eloped as he companion app o he D eamMachine, is buil o And oid de ices using he Ja a p og amming language. I s p ima y unc ions include ecei ing and decoding comp essed EEG da a, eal- ime signal isualiza ion, eco ding managemen , and con igu a ion o he D eamMachine ha dwa e. Use s can adjus pa ame e s such as sampling a e, bi esolu ion, and il e se ings di ec ly wi hin he app. EEGD oid also se es as an educa ional ool, o e ing basic EEG knowledge and use guidance ia i s Gi Hub eposi o y. To assess pe o mance ac oss de ices, he applica ion was es ed on wo And oid able s, including Samsung Galaxy Tab S4 (And oid 10) and Samsung Galaxy Tab S6 (And oid 12). In bo h cases, he app main ained s able BLE connec ions, displayed eal- ime EEG da a wi hou delay, and eliably sa ed da a iles. No compa ibili y issues o c ashes we e obse ed du ing con igu a ion o eco ding sessions, demons a ing ha EEGD oid pe o ms consis en ly ac oss di e en ha dwa e and ope a ing sys em e sions wi hin he es ed ange. 2.3.1. Gain and bi s pe channel The AD7779 componen used in he D eamMachine includes a p og ammable gain ampli ie (PGA) wi h selec able gains o 1, 2, 4, and 8. These gain se ings scale he inpu signal be o e digi iza ion, e ec i ely inc easing he esolu ion o low-ampli ude signals such as EEG. Fo example, wi h a e e ence ol age o 2.5 V, using gain =1 gi es a ull inpu ange o ±2.5 V and a esolu ion o abou 0.298 μ V pe LSB. Using gain =8 educes he inpu ange o ±312.5 mV, bu inc eases he esolu ion o abou 0.037 μ V pe LSB. The ou pu emains 24-bi (16,777,216 codes), bu he LSB size changes based on gain. These se ings a e con igu able in he EEGD oid applica ion o allow use s o op imize o p ecision based on signal cha ac e is ics. The e m “bi s pe channel” e e s o he numbe o digi al bi s used o ep esen he ampli ude o he EEG signal eco ded om each channel. In EEG acquisi ion, analog signals gene a ed by b ain ac i i y a e sampled and con e ed o digi al alues. A highe bi dep h allows o ine esolu ion and a b oade dynamic ange, meaning smalle ol age di e ences can be de ec ed. Wi hin he EEGD oid applica ion, EEG da a—o iginally cap u ed by he AD7779 chip a a high 24-bi esolu ion—is commonly downsampled o 10, 14, o 16 bi s pe channel o ansmission and s o age. This downsampling helps educe memo y usage, bandwid h demands, and p ocessing load. Fo con ex , a 10-bi signal can ep esen alues om 0 o 1023, a 14-bi signal om 0 o 16,383, and a 16-bi signal om 0 o 65,535. While highe bi dep hs p ese e mo e de ail and dynamic ange, hey also gene a e signi ican ly la ge da a s eams. The e o e, he selec ed bi dep h e lec s a balance be ween he desi ed signal p ecision and he limi a ions o he ha dwa e o so wa e en i onmen . 2.3.2. Fil e ing Fil e ing o EEG signals is a c i ical s ep in da a p ep ocessing o expe imen s. Because EEG signals a e low ampli ude (in he P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 4 mic o ol ange), hey a e p one o noise con amina ion om elec ical de ices, muscle mo emen s, and en i onmen al ac o s. Fil e ing is used o elimina e hese unwan ed componen s, imp o ing he da a’s signal- o-noise a io (SNR). Speci ic equency bands can also be selec i ely ampli ied o a enua ed o enhance desi ed EEG componen s, such as b ain wa es. Fil e ing also assis s in de ec ing and emo ing a i ac s like eye blinks, eye mo emen s, and elec ode pops, aiding in he accu a e in e p e a ion o b ain ac i i y. Fu he mo e, il e ing s anda dizes EEG da a ac oss a ious eco ding condi ions and subjec s, acili a ing easie compa ison o esul s ac oss s udies. Fo hese easons, D eamMachine o e s con igu able se ings wi h a ange o il e ing op ions, enabling use s o selec he op imal se ings o achie ing high-quali y EEG signals. Da a is ini ially sampled a a a e o 500 Hz, wi h il e s applied be o e he da a is downsampled o 250 Hz o ansmission. The de aul il e s include an in ini e impulse esponse (IIR) il e , a ou h-o de high-pass il e wi h a cu o equency o 1 Hz, a ou h- o de low-pass il e wi h a cu o equency o 45 Hz, and a band-s op il e wi h a ange o 46-54 Hz. The e ec o he de aul il e s is demons a ed in Fig. 2, which shows ha he D eamMachine e ec i ely elimina es he 50 Hz noise caused by powe line in e e ence. O he il e ing op ions o he D eamMachine p o ides a e lis ed in Table 1. Use s can cus omize hese se ings o disable he il e s en i ely, depending on he needs o hei expe imen . While ou es ing sugges s ha he de aul il e ing con igu a ion p o ides op imal pe o mance in mos cases, he ideal se up ul ima ely depends on he speci ic esea ch ques ion and should be adjus ed acco dingly. 2.3.3. Bi shi and sa e y ac o E en i only he di e ences be ween consecu i e 24-bi eco dings we e ansmi ed, he esul ing alues would s ill equi e ull 24- bi ep esen a ion. This imposes limi s on ei he he sampling a e o he numbe o channels ha can be suppo ed due o bandwid h Fig. 2. A sweep signal anging om 1 o 70 Hz is gene a ed using a signal gene a o and injec ed in o he D eamMachine. The expe imen is conduc ed wice: once wi h he de aul il e se ing ( il e on) and once wi h all il e s disabled ( il e o ). The blue line ep esen s he sweep signal om 1 o 70 Hz, while he o ange line shows he powe a enua ed a 50 Hz when he il e is ac i e. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 5 Fig. 2. (con inued). Table 1 Fil e op ions p o ided by he D eamMachine sys em. High-pass Fil e Low-pass Fil e Band-s op Fil e 0.8 Hz o de 2 45 Hz o de 4 46–54 Hz o de 6 1.0 Hz o de 4 60 Hz o de 6 46–54 Hz o de 4 1.7 Hz o de 4 −48–52 Hz o de 6 1.7 Hz o de 2 −48–52 Hz o de 4 o o o Fig. 3. Bi shi Diag am: The calcula ed di e ence be ween S1 and S2 is a smalle alue s ill ep esen ed in 24-bi . When he di e ence is small, he leading bi s a e ze os and ca y no in o ma ion. In con as , when he di e ence is la ge, he ini ial bi s con ain signi ican in o ma ion, while he la e bi s, ep esen ing e y ine ol ages, can be neglec ed. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 6 cons ain s. To add ess his, he sys em acks a loa ing a e age o he signal on he de ice, and each second, i calcula es he s anda d de ia ion o he signal. This alue is hen used o iden i y he mos ele an po ion o each 24-bi di e ence o e icien ansmission. Ins ead o sending he ull 24-bi di e ence, a bi shi alue is ansmi ed alongside he comp essed da a. This bi shi indica es how many o he mos signi ican bi s ( om he le ) a e skipped. As shown in Fig. 3, a la ge bi shi enables ansmission o la ge signal changes bu educes esolu ion. In con as , a smalle bi shi p o ides highe p ecision bu limi s he ange o ep esen able di e ences. Each EEG channel de e mines i s op imal bi shi indi idually based on i s ecen ac i i y. This bi shi is sen o he companion applica ion, whe e i is used o econs uc he signal by applying he shi and adding he esul o he p e ious alue. Ad anced use s can con igu e a maximum bi shi o apply a sa e y ac o based on expec ed signal cha ac e is ics. Limi ing he maximum bi shi is especially use ul in ypical EEG eco dings (e.g., om heal hy pa icipan s), whe e la ge jumps o en indica e a i ac s—such as hose caused by muscle ac i i y, eye blinks, o elec ode dis u bances. In such cases, cu ing o hese peaks does no signi ican ly impac use ul da a. By se ing a maximum bi shi , he sys em a oids alloca ing bandwid h o po en ially non-in o ma i e la ge changes and ins ead p ese es esolu ion o subsequen samples. The sa e y ac o speci ies how much g ea e a single signal change can be ela i e o he s anda d de ia ion measu ed o e he p e ious second. Fo example, a sa e y ac o o 8 means ha di e ences up o eigh imes he ecen s anda d de ia ion can be ansmi ed wi hou unca ion. To achie e his, ou addi ional high-o de bi s a e used beyond wha he algo i hm would no mally alloca e. While his educes o e all esolu ion, i p e en s loss o c i ical da a du ing sudden la ge changes. This is pa icula ly ele an in clinical applica ions such as seizu e de ec ion, whe e la ge ampli ude spikes may ca y diagnos ic alue and should no be disca ded as a i ac s. The maximum ol age di e ence ha can be ansmi ed depends on he numbe o bi s used o encoding he di e ence, and is calcula ed using: Δmax =2×V e 2b whe e VREF =2.5 V and b is he numbe o bi s used o ep esen he signal, he minimum de ec able ol age change pe ansmi ed Fig. 4. This igu e displays he EEGD oid And oid applica ion, which is gene a ing a da a loss no i ica ion. I indica es ha 3% o he samples a e being los and sugges s changing he cu en se ings o p e en u he da a loss. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 7 s ep depends on he esolu ion. Fo example, wi h 16-bi esolu ion, he smalles s ep is 2×V e 216 =76.3 μ V, and wi h 24-bi esolu ion, i is 2×V e 224 =298.0nV. These alues ep esen he smalles ol age di e ences ha can be de ec ed and ansmi ed, helping o de ine how much signal de ail is p ese ed o los unde di e en comp ession s a egies. 2.3.4. Da a o ma s Facili a ing he u iliza ion o ou de ice by nume ous esea che s and ensu ing seamless da a managemen in acco dance wi h es ablished EEG s anda ds has been a pa amoun conside a ion. Consequen ly, we ha e inco po a ed wo me hods o eco ding and s o ing da a. The i s me hod in ol es he s aigh o wa d sa ing o eco dings di ec ly on o he sma phone o able in “CSV” o ma . Al e na i ely, pa icula ly du ing expe imen al scena ios, da a can be s eamed h ough LabS eamingLaye (LSL) [50]. This app oach enables impeccable synch oniza ion wi h igge s and o he expe imen al inpu s essen ial o he p ocessing o EEG da a. The LSL o ma adhe es o indus y s anda ds, speci ically he “xd ” o ma , and allows o con enien u he p ocessing using popula EEG ools such as Field ip and EEGLab [45,46]. 2.4. Package loss One o he goals o he D eamMachine sys em is o enable a b oad ange o people o use he EEG de ice. The e o e, he de ice should no only wo k wi h powe ul end de ices wi h high CPU capabili ies and he la es Blue oo h echnology bu also yield sa is ac o y esul s wi h olde de ices. Howe e , i sub-op imal se ings a e selec ed, da a packages can be los due o a ull Blue oo h bu e o an o e loaded CPU o he ecei ing de ice. As discussed be o e, he decoding algo i hm does no pe o m op imally i packages a e los since a co ela ion be ween subsequen eco dings is assumed. This e ec is exace ba ed i he packages a e no e enly sampled and a e los in a se . The e o e, p e en ing package loss is p e e ed o e main aining a lowe sampling a e, e en i he absolu e numbe o ansmi ed packages migh be smalle . To main ain op imal pe o mance, he app ac i ely moni o s he occu - ence o da a package loss and, when necessa y, p o ides ecommenda ions o swi ching o al e na i e se ings, as depic ed in Fig. 4. 2.5. S a e o he a mobile EEG de ices In ecen yea s, he e has been a g owing in e es in using mobile EEG sys ems ac oss a wide ange o expe imen s. This inc eased in e es is d i ing he de elopmen and inno a ion o new mobile EEG echnologies [44]. A s anda dized me hod o e alua ing and compa ing mobile EEG sys ems ac oss di e en s udies is essen ial o selec ing he app op ia e sys em o esea ch o de eloping he nex gene a ion o mobile EEG echnology. The Ca ego iza ion o Mobile EEG (CoME) scheme was in oduced o add ess his need. This scheme classi ies mobile EEG de ices based on se e al key pa ame e s, including de ice mobili y, pa icipan mobili y, sys em con igu a ion (such as he numbe o channels, sampling a e, esolu ion, and o he echnical ea u es), and con ex ual ac o s ha may in luence he usabili y and pe o mance o he EEG sys em. By e alua ing hese aspec s, he CoME scheme aims o enhance he quali y and eliabili y o eco ded da a and ensu e he p ac ical applica ion o mobile EEG echnology. In he pape by An hony Ba eson and his colleagues [27], he CoME sco es o 15 mobile EEG expe imen s conduc ed wi h i e di e en de ices de eloped o e he pas wel e yea s we e e alua ed, including hei own. The a e age CoME sco es o hese de ices a e as ollows: 3.5 ou o 5 o de ice mobili y (D), 1 ou o 5 o pa icipan mobili y (P), 9 ou o 20 o sys em speci ica ion (S) (wi h he mean used when anges we e p o ided), and a channel coun (C) o 10.3. In compa ison, ou de ice sco es 4D, 1P, 10-12S, and 24C. The bi esolu ion is ambiguous, as we sample a 24-bi esolu ion (sco ing 4) bu ansmi a 16-bi esolu ion (sco ing 2). Conside ing his, D eamMachine mee s o exceeds he a e age pe o mance o cu en mobile EEG de ices in he e alua ed ca ego ies. The ollowing pa ag aph highligh s h ee ecen de ices o unde sco e he unique ea u es o D eamMachine. I demons a es ha D eamMachine is unpa alleled in being open- sou ce and cos -e ec i e while main aining pe o mance compe i i e wi h comme cial mobile EEG sys ems. In compa ison o he mobile EEG sys em p esen ed by An hony Ba eson [27], se e al dis inc app oaches and ad an ages a e o e ed by D eamMachine. The key di e ences include he use o Wi-Fi ins ead o Blue oo h, he implemen a ion o he companion applica ion in C# a he han Ja a, and he unique me hod o ansmi ing expe imen al igge s. In An hony Ba eson’s s udy, Wi-Fi was used because o i s highe bandwid h compa ed o Blue oo h. Howe e , in D eamMachine, an inno a i e encoding scheme e ec i ely mi iga es Blue oo h’ slowe bandwid h and le e ages he ad an ages o BLE. Fo u he de ails, see Blue oo h sec ion. The decision o implemen he applica ion in Ja a was made due o i s widesp ead use and p e alence. Da a indica es ha Ja a is he second mos lea ned p og amming language a e Py hon [28]. The e sa ile, open-sou ce applica ion de eloped in a widely known p og amming language allows esea che s o easily cus omize he applica ion o sui hei expe imen al needs. An addi ional ad an age o he D eamMachine lies in he seamless s o age o igge s. Unlike he de ice p esen ed in An hony Ba eson’s s udy, whe e igge s a e sen ia a designa ed channel, D eamMachine is compa ible wi h LabReco de and capable o s eaming da a using LSL. This compa ibili y enables esea che s o con inue using es ablished me hods o conduc ing expe imen s. Mo eo e , in his s udy, a p o o ype o he applica ion has been de eloped o sa e igge s and expe imen al cues di ec ly as a CSV ile, allowing all channels o be u ilized o eco ding EEG da a. Al hough Ba eson’s p ojec inco po a es an impedance check wi hin hei companion app—a ea u e ha aids in se ing up connec ions and imp o ing eco ding quali y— his unc ionali y is cu en ly absen in he D eamMa- chine sys em. Howe e , he inclusion o his aluable ea u e is planned o a u u e e sion o he applica ion. A widely used and well-es ablished mobile EEG de ice is he SMARTING mobi3, which has been employed in a ious esea ch con ex s, including audi o y a en ion decoding, saliency de ec ion, isual selec i e a en ion asks, and BCI de elopmen [29–31]. The sys em eco ds 24 channels wi h sampling a es be ween 250 and 500 Hz and is compa ible wi h he Lab S eaming Laye (LSL) [32]. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 8 The manu ac u e also o e s an ad anced e sion, SMARTING p o4, which includes addi ional ea u es such as 32-channel suppo , sampling a es up o 1000 Hz, 3D mo ion acking, au oma ic a i ac emo al, and onboa d da a s o age. In con as o comme cially closed sys ems like SMARTING, he D eamMachine was de eloped wi h a di e en se o goals, pa icula ly ocusing on open-sou ce accessibili y, cos -e ec i eness, and cus omizabili y. The D eamMachine’s ha dwa e and so wa e a e openly documen ed, allowing esea che s o inspec , adap , and ex end he sys em o sui hei speci ic expe imen al o educa ional needs. This makes i especially sui able o eaching en i onmen s, hacka hons, and esea ch in esou ce-limi ed se ings. While SMARTING sys ems o e ad anced echnical capabili ies and high-pe o mance speci ica ions, a di ec pe o mance compa ison is beyond he scope o his pape . Ins ead, Table 2 Design ile summa y. Design ile name File ype Open-sou ce license Loca ion o he ile PCB design .b d .sch .pd .cs .zip GNU Gene al Public License 3.0 h ps://doi.o g/10.5281/zenodo.14627406 h ps://gi hub.com/pa ia-samimi/d eam-machine-eeg/ ee/main/Boa d% 20Design Fi mwa e code The sou ce code is in he C p og amming Language GNU Gene al Public License 3.0 h ps://doi.o g/10.5281/zenodo.14627406 h ps://gi hub.com/pa ia-samimi/T aumsch eibe / ee/mas e / T aumsch eibe _BLE_Code App Sou ce code The sou ce code is in Ja a P og amming Language GNU Gene al Public License 3.0 h ps://doi.o g/10.5281/zenodo.14627406 h ps://gi hub.com/denizmgun/EEG-D oid/ ee/ d72 b9ea78de1 27 e0690aa12 09323 0b82c2 Table 3 Bill o ma e ials summe y. Designa o Componen Numbe Cos pe uni (Eu o) To al cos ¡ (Eu o) Sou ce o ma e ials Ma e ial ype ADC1-ADC3 (Analog o Digi al) AD7779ACPZ ADC Chip 3 12.00 36.00 Link Semiconduc o J1 (Heade ) SHF-105–01-L-D-SM-TR 1 2.50 2.50 Link Me al BC832 (Blue oo h) BC832 1 10.2 10.2 Link O he CLOCK DSC1001DI5-008.1920 T 1 1.8 1.8 Link Semiconduc o CHARGER BQ24072TRGTR 1 1.8 1.8 Link Semiconduc o RESET TL3780AF330QG 1 0.26 0.26 Link Semiconduc o ESD1-ESD7 (ESD p o ec ion diodes) TPD4E1B06DRLR 7 0.4 2.8 Link Semiconduc o Mic o USB Type B Connec o s 2040002–1 1 1.8 1.8 Link O he Ba e y Holde 1051 1 1.7 1.7 Link Plas ic Fig. 5. 3D iew o he D eamMachine PCB. The le image displays he op side o he PCB, while he igh image shows he bo om side, high- ligh ing he assembled componen s and ci cui layou . P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 9 wi h selec ing and a aching EEG elec odes o he subjec ’s scalp. Use s can choose om a a ie y o elec ode ypes, depending on he needs o he s udy and a ailable equipmen . Elec odes may be placed using a s anda dized cap o manually posi ioned acco ding o he 10–20 sys em. The D eamMachine suppo s up o 24 channels, al hough use s a e no equi ed o u ilize all o hem. The sys em is designed o comple e lexibili y, allowing esea che s o selec only he numbe and loca ions o channels ha a e ele an o hei speci ic expe imen al design. Re e ence and g ound elec odes a e ypically placed on he mas oid bones behind he ea s, al hough his can be adjus ed o mee he s anda ds o he esea ch p o ocol. The o he ends o he elec odes a e secu ely connec ed o he D eamMachine’s 26-pin inpu in e ace. Once all elec odes a e in place, he de ice is powe ed on. The EEGD oid companion app should be ins alled on an And oid de ice in ad ance o use. A e u ning on he D eamMachine, he app de ec s he de ice ia Blue oo h and enables a seamless pai ing p ocess. Use s can hen con igu e de ice pa ame e s in he app, including sampling a e, esolu ion, gain, il e s, and o he se ings. Al hough a ecommended de aul con igu a ion is a ailable, all se ings a e ully use -adjus able o accommoda e di e se expe imen al needs. In Fig. 7, he p o o ype o he EEGD oid applica ion is p esen ed. The p o o ype And oid applica ion is designed o eco d EEG signals on And oid de ices. The p ocess o using, connec ing, and con igu ing he applica ion is demons a ed h ough igu es. The i s menu o he EEGD oid applica ion is accessible a he op le co ne , whe e he “Reco d” bu on can be selec ed o ini ia e he eco ding p ocess. Upon selec ing “Reco d,” he display menu is shown. To connec he D eamMachine ha dwa e o he applica ion, he Blue oo h symbol on he op igh mus be selec ed. When he D eamMachine ha dwa e sys em is powe ed on, he name “T aumsch eibe _ ” appea s in he lis o de ec ed Blue oo h de ices. Once selec ed, he “Gea ” icon, loca ed nex o he Blue oo h icon, allows con igu a ion o he applica ion o op imize EEG signal quali y. Con igu a ion op ions a e displayed in he accompanying images, wi h u he de ails explained in subsequen sec ions. A e he app op ia e con igu a ion is selec ed, EEG signals can be isualized on he sc een by selec ing he “Bell” symbol loca ed a he op igh . All 24 channels a e displayed in dis inc colo s, and indi idual channels can be clicked o assess signal quali y and beha io . The eco ding p ocess begins by selec ing he ed bu on a he bo om le . Upon comple ion o he expe imen , eco ding can be s opped, and eco dings can be labeled as needed. The applica ion p o ides a summa y o he eco ding’s pe o mance and sa es he da a in “CSV” o ma o u he analysis. Fig. 9. (con inued). P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 16 7. Valida ion and cha ac e iza ion Wi h he inc easing a ailabili y o low-cos and mobile EEG sys ems, a g owing numbe o s udies ha e assessed hei signal quali y in compa ison o esea ch-g ade equipmen [47–49]. Fo ins ance, Melnik e al. (2017) examined a iabili y ac oss di e en EEG sys ems ela i e o he a iabili y obse ed be ween subjec s and ac oss epea ed sessions, using he same expe imen al pa adigm applied o ou di e en de ices [47]. This ype o compa a i e alida ion helps cla i y how sys em- ela ed ac o s in luence EEG da a quali y. Building on his app oach, he cu en s udy employed he well-es ablished eyes-open/eyes-closed pa adigm o e alua e he D eamMachine’s pe o mance ela i e o a s anda d clinical-g ade EEG sys em. This ask is equen ly used in alida ion s udies due o he obus and p edic able inc ease in alpha-band powe obse ed du ing eye closu e—a neu al ma ke widely suppo ed in he li e a u e [48,49]. The alida ion was conduc ed a he EEG labo a o y o Osnab ück Uni e si y, whe e he D eamMachine was es ed alongside he asalab™ sys em om ANT Neu o. The same pa icipan s comple ed he eyes-open and eyes-closed ask wi h bo h sys ems unde ma ched expe imen al condi ions. The esul ing alpha-band di e ences be ween eyes-open and eyes-closed condi ions we e analyzed o assess he D eamMachine’s abili y o de ec es ablished neu al pa e ns and manage baseline noise le els e ec i ely. These indings p o ide p elimina y e idence ha he D eamMachine can p oduce eliable EEG eco dings sui able o cogni i e pa adigms, aligning wi h ea lie demons a ions o he capabili ies o o he mobile EEG pla o ms [48,49]. Ten subjec s, i e males and i e emales aged be ween 18 and 25 yea s wi h no mal o co ec ed ision, pa icipa ed in he s udy. Acco ding o p e ious s udies, a sample size o 10 subjec s is commonly selec ed o expe imen s in ol ing eyes-open and eyes-closed condi ions, as i has been shown o be su icien o de ec ing meaning ul di e ences in neu al ac i i y. These s udies sugges ha , gi en he con olled na u e o he expe imen and he ocus on indi idual b ain esponses, a sample size o 10 p o ides eliable and alid esul s wi hou he need o la ge g oups [47,48]. Be o e commencing he expe imen , pa icipan s we e no subjec ed o neu ological, ch onic, o psychological e alua ions. They we e eques ed o disclose any such condi ions i applicable. None o he pa icipan s epo ed any disabili ies o he a o emen ioned medical issues o hei knowledge. Fu he mo e, pa icipan s we e ins uc ed o abs ain om consuming be e ages con aining alcohol o ca eine o a leas h ee hou s be o e he commencemen o he Fig. 9. (con inued). P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 17 expe imen . Fo a mo e accu a e compa ison, he same cap ( he wa egua d™o iginal ANT Neu o EEG) was used, and he expe imen was conduc ed in he same loca ion, wi h he same subjec s, and unde he same condi ions. The expe imen in ol ed pa icipan s closing and opening hei eyes o wo-minu e in e als, epea ed i e imes. This esul ed in one pa o he expe imen las ing 20 min, and he en i e expe imen aking 40 min o each subjec . In his s udy, he expe imen commenced wi h he eyes-closed condi ion. The used cap, allowed o he con inuous eco ding o signals om 24 elec odes, which we e posi ioned by he in e na ional 10–20 sys em. Simila esea ch on D eamMachine and s anda d EEG de ices o a single subjec has been p e iously conduc ed and documen ed [16]. The p ima y objec i e o he cu en s udy was o ensu e he compa abili y be ween he D eamMachine and a con en ional EEG de ice ac oss a la ge numbe o subjec s, he eby demons a ing he eliabili y o he D eamMachine in a mo e ex ensi e sample. 7.1. Me hodology Each subjec was sea ed on a chai a a dis ance o 90 cm om he moni o , wi h he expe imen ecei ing app o al om he Osnab ueck Uni e si y E hics Commi ee o he P o ec ion o Human Subjec s. 24 channels o he wa egua d™ o iginal ANT Neu o EEG cap a e connec ed o he subjec s, and o all subjec s, he le ea lobe was used as a e e ence poin , while he igh ea lobe se ed as he g ound. In he ini ial phase o he expe imen , eco dings we e made using he S anda d EEG sys em. The cap was a ixed and connec ed o an ampli ie sys em, acili a ing signal ansmission o a compu e placed in he labo a o y. Fig. 8 illus a es he se up o he expe imen . Subsequen ly, he eco ded da a was p ocessed and s o ed using he ASALAB analysis so wa e and da a acquisi ion occu ed a a sampling a e o 1024 Hz. Once all necessa y s eps we e comple ed, he impedance o all 24 channels was checked using ASALAB so wa e o ensu e i emained below 5kΩ. Upon ensu ing all channels we e co ec ly connec ed, he subjec was ins uc ed o look le and igh and blink o e i y dissimila beha io in all channels, con i ming he p ope connec ion o he e e ence and g ound. Nex , he subjec was ained on he expe imen p o ocol. The expe imen was ini ia ed a e con i ming ha all equipmen was p ope ly connec ed and unc ioning. The s anda d EEG sys em was used i s o all subjec s, ollowed by he D eamMachine. In he case o D eamMachine, i was wi elessly linked ia Blue oo h o a able (Samsung Galaxy Tab S6 Li e, 64 GB) unning he EEGD oid applica ion, acili a ing he eco ding and s o age o he EEG signal. The de aul eco ding se ings in he EEGD oid Fig. 9. (con inued). P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 18 applica ion included a no ch il e wi h a equency ange om 46 Hz o 54 Hz ( ou h-o de il e ), a gain le el o 8, and a low-pass il e se a 60 Hz (six h-o de il e ) was used o his expe imen . Addi ionally, da a acquisi ion was pe o med a a sampling a e o 250 Hz, wi h he LSL- eco de applica ion [50], which was used o eco d and sa e he da a as an xd ile. T igge s we e sys ema ically documen ed in bo h phases o he expe imen o deno e ins ances o pa icipan s closing o opening hei eyes. In he eyes-open condi ion, pa icipan s we e equi ed o di ec hei gaze owa d a ixa ion c oss displayed on he moni o , and in bo h condi ions, hey we e asked no o mo e hei body pos u e. 7.2. Da a ex ac ion The eco ded da a we e ob ained in wo di e en o ma s due o he use o di e en de ices. The s anda d EEG sys em p oduced da a in he .cn o ma , while he D eamMachine gene a ed da a in he .xd o ma . Ini ially, he da a we e impo ed in o MATLAB using Field ip in wo sepa a e iles, a e which he same sc ip was used o hei analysis. Fi s , a isual compa ison was made be ween he plo s gene a ed by he D eamMachine sys em and hose p oduced by he s anda d EEG sys em in ime domain. This in ol ed sc u inizing he plo s om he D eamMachine sys em o iden i y p ominen ea u es unde wo di e en condi ions (eyes- open, eyes-closed), such as eye mo emen s, eye blinks, Alpha wa es, and Del a wa es. Subsequen ly, he same analysis was conduc ed on he s anda d EEG sys em’s plo s. Following his, he da a we e denoised and analyzed in bo h ime and equency domains. Du ing he eco ding p ocess, he EEGD oid applica ion il e ed ou he al e na ing cu en (AC) o clean he da a and elimina e noise, so his s ep was omi ed om subsequen p ep ocessing s eps. In he nex s age, a e excluding noisy channels, he signal was segmen ed in o 40 sec ions, esul ing in 30-second epochs o bo h eyes-open and eyes-closed condi ions. Following his, he powe spec um o each epoch was calcula ed using he Fas Fou ie T ans o m (FFT). Then, he same s a is ical analysis was applied o bo h de ices. To add ess he mul iple compa ison p oblem (MCP) associa ed wi h mul idimensional da a, a igo ous clus e -based pe - mu a ion es was employed. This es aimed o de e mine he signi icance p obabili y (p- alue) o mul iple ime poin s by compu ing clus e s, a he han conduc ing indi idual s a is ical es s o each ime poin . Essen ially, clus e s we e compu ed based on - alues, hen hei pe mu a ion was de e mined, and he maximum alues we e used o calcula e he signi icance p obabili y. Fo bo h Fig. 9. (con inued). P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 19 da ase s, he - alue was de i ed om he independen samples - es , wi h an alpha h eshold se a 0.01 o bo h condi ions. Addi ionally, he Mon e Ca lo me hod was used o calcula e he signi icance p obabili y o he clus e s. The esul s o he clus e -based pe mu a ion es we e hen p esen ed in a opog aphical plo , illus a ing powe spec a o bo h condi ions. The sc ip o analyzing he expe imen is a ailable on a Gi Hub eposi o y. In summa y, an analysis was conduc ed on each plo gene a ed by he D eamMachine sys em o iden i y he p esence o he Alpha hy hm in he occipi al head channels du ing he eyes-closed condi ion, as well as de ec ing eye blinking, eye mo emen s, and Del a hy hm du ing he eyes-open condi ion. This p ocess was hen epea ed o he plo s gene a ed by he clinical EEG sys em. I was subsequen ly de e mined whe he he wo sys ems exhibi ed analogous componen s in each co esponding epoch o i speci ic ea u es we e unique o ei he o he eco dings. 7.3. Da a analysis The p ep ocessing s eps we e comple ed a e he da a we e di ided in o di e en condi ions. The da a we e hen plo ed in he ime domain o compa e he signals om bo h EEG sys ems. The signals om all subjec s, eco ded wi h bo h sys ems, we e plo ed and isually inspec ed. The beha io o he signals in bo h eyes-open and eyes-closed condi ions was simila ac oss bo h sys ems. Fo da a o be p ope ly analyzed in he equency domain, all cleaned da a se s we e analyzed using he mul i- ape me hod wi h Fas Fou ie T ans o m (FFT) o compu e powe spec a. To achie e be e esul s, he da a we e examined in a ious equency bands o gain insigh s in o b ain ac i i y beha io unde di e en condi ions. The selec ed equency bands in his p ojec a e Del a (1–4 Hz), The a (4–8 Hz), Alpha (8–12 Hz), and Be a (12–30 Hz). Finally, a clus e -based pe mu a ion es was employed o compa e he powe spec al densi y be ween he ‘open’ and ‘closed’ condi ions ac oss he 24 channels [16]. As a esul , se en channels, namely FP1, FP2, FPz, POz, O1, O2, and OZ, a e selec ed as he mos e ec i e channels. The esul s o he analysis a e p esen ed in he nex sec ion. Fig. 9. (con inued). P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 20 7.4. Resul s The esul s o he clus e -based pe mu a ion es o eigh pa icipan s a e shown in Fig. 9. I is e iden ha simila b ain ac i i y signals we e cap u ed by bo h EEG de ices. In he eyes-closed condi ion, a signi ican inc ease in ac i i y (a ound 10 Hz) was obse ed in he occipi al egion o all pa icipan s. This inding highligh s he p e alence o alpha ac i i y du ing es ing s a es. Addi ionally, inc eased ac i i y in he on al egion was e iden in he eyes-open condi ion, co esponding o heigh ened isual ac i i y du ing eyelid mo emen and in o ma ion p ocessing ac oss he en i e co ex. Despi e some noise being p esen in he signals om bo h de ices, hei consis ency ac oss condi ions is appa en . Fig. 10 showcases opog aphical plo s ha isually ep esen s a is ically signi ican di e ences in powe spec al densi y be ween he “eyes closed minus eyes open” and “eyes open minus eyes closed” condi ions in bo h EEG sys ems. Dis inc colo -coded egions highligh clus e s o elec odes wi h signi ican di e ences, wi h mo e in ense colo s signi ying lowe p- alues (p <0.01). These indings indica e ha highe powe spec al densi y is p edominan ly obse ed in he occipi al egion. I is also highligh ed ha he same beha io is obse ed in bo h EEG sys ems, which con i ms ha he esul s o bo h EEG sys ems a e oughly he same. The inal esul is illus a ed in Fig. 11 h ough a powe spec a diag am, showing he ela ionship be ween powe and equency. Fo bo h de ices, he e is no able powe in he ange o 0 Hz – 4 Hz (Del a band), pa icula ly in he eyes-open condi ion. This inc eased powe in he del a band in bo h s anda d EEG and D eamMachine sys ems in eyes-open condi ion aligns wi h expec ed pa e ns a ising om heigh ened co ical ac i i y du ing in o ma ion p ocessing and isual s imulus ecep ion. Addi ionally, a signi ican peak in powe is obse ed a ound 10 Hz o he eyes-closed condi ion, a ibu ed o p onounced alpha ac i i y du ing es , consis en wi h he Be ge e ec and an icipa ed esul s. O e all, bo h EEG sys ems p oduced nea ly iden ical ou comes ac oss bo h condi ions [51]. 8. Discussion The D eamMachine was de eloped as an accessible, open-sou ce, and cus omizable mobile EEG sys em ha balances a o dabili y wi h unc ional pe o mance o a wide ange o EEG applica ions. I s p ima y goal is o se e as a lexible pla o m o educa ional use, Fig. 9. (con inued). P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 21 explo a o y esea ch, and apid p o o yping in neu oscience and neu o echnology. One o i s key s eng hs is i s open design; all ha dwa e and so wa e componen s a e ully documen ed and modi iable, allowing use s o explo e sys em a chi ec u e, ailo he pla o m o hei speci ic needs, and con ibu e o i s ongoing de elopmen . I s low cos u he enhances i s accessibili y, making i pa icula ly well sui ed o class oom en i onmen s, wo kshops, hacka hons, and esea ch p ojec s in esou ce-limi ed se ings. The po en ial o he D eamMachine sys em o cogni i e s udies is suppo ed by he expe imen al esul s p esen ed in his pape . In he eyes-open/eyes-closed pa adigm, bo h he D eamMachine and a s anda d mobile EEG sys em consis en ly demons a ed he ex- pec ed inc ease in alpha-band powe du ing eye closu e, a well-es ablished indica o o neu al ac i i y. This consis ency ac oss sys ems con i ms ha he D eamMachine can eliably cap u e key EEG ea u es. While i is no in ended o ma ch he pe o mance o high-end sys ems, hese esul s sugges ha D eamMachine is well sui ed o cogni i e neu oscience applica ions, pa icula ly in con ex s whe e cos o accessibili y a e limi ing ac o s. One no able limi a ion o he D eamMachine sys em is he absence o an impedance checking ea u e. While he companion app p o ides a li e iew o each channel’s signal o isual inspec ion, his me hod is subjec i e and may be insu icien o de ec ing poo elec ode con ac —especially o inexpe ienced use s. Wi hou au oma ed impedance eedback, issues such as high elec ode impedance o poo connec ions may go unno iced, po en ially deg ading da a quali y and a ec ing he accu acy o EEG analysis. This limi a ion is pa icula ly ele an in ield o educa ional se ings, whe e use s may lack he expe ise o iden i y and esol e such Fig. 10. An ex ensi e s a is ical analysis was pe o med using he clus e -based pe mu a ion es . The igh side displays he esul s o “eyes open minus eyes closed,” while he le side shows he esul s o “eyes closed minus eyes open.” Panel A p esen s da a om he S anda d EEG sys em, whe eas Panel B highligh s he esul s om he D eamMachine sys em. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 22 p oblems in eal ime. Ano he cons ain ela es o he numbe o channels. Al hough D eamMachine o e s 24 EEG channels, which is su icien o many s anda d expe imen s, i may no p o ide he spa ial esolu ion equi ed o ad anced sou ce localiza ion o ICA- based a i ac ejec ion ha bene i s om highe channel densi y. Fu u e wo k will include o mal compa isons be ween he D eamMachine and o he mobile EEG sys ems ac oss a wide ange o expe imen al asks. In pa allel, e o s a e unde way o de elop enhanced so wa e ools o a i ac ejec ion and signal in e p e a ion, as well as o explo e in eg a ion wi h wea able and IoT-based echnologies. In addi ion, compa a i e es s ha e al eady been con- duc ed be ween he D eamMachine and o he sys ems—such as he Somno HD Eco [52]—in he con ex o sleep- ela ed s udies in ol ing nigh ma es and sleep diso de s. The esul s will be de ailed in a o hcoming publica ion. Finally, he D eamMachine’s po en ial o ambula o y moni o ing and use in ecologically alid, eal-wo ld se ings will be u he in es iga ed h ough long- e m s udies. E hics s a emen The D eamMachine is a de ice ha esembles a s anda d medical de ice and is in ended solely o educa ional and expe imen al use, de eloped wi h adhe ence o biomedical sa e y s anda ds. The pa icipan s in his s udy we e esea che s om elec onic and cogni i e science ields. They we e ully in o med abou he de ice’s pu pose and he p ocedu es in ol ed, and w i en in o med consen was ob ained be o e pa icipa ion. The olun ee s we e heal hy indi iduals wi h no medical his o y o epilepsy o o he condi ions ha could impac he expe imen al p ocedu es, and no sca s we e p esen in he expe imen . The esea ch was conduc ed in compliance wi h he Decla a ion o Helsinki and app o ed by he E hics Commi ee o Osnab ück Uni e si y. This ensu es he s udy aligns wi h e hical s anda ds o esea ch in ol ing human subjec s. CRediT au ho ship con ibu ion s a emen Pa ia Samimisabe : W i ing – e iew & edi ing, W i ing – o iginal d a , Visualiza ion, So wa e, Me hodology, Da a cu a ion, Concep ualiza ion. Lau a K iege : W i ing – e iew & edi ing, W i ing – o iginal d a , So wa e, Me hodology, Da a cu a ion. Ma c Vidal De Palol: W i ing – e iew & edi ing, So wa e, Resou ces, In es iga ion. Deniz Gün: W i ing – e iew & edi ing, So wa e, Fo mal analysis. Go don Pipa: W i ing – e iew & edi ing, Supe ision, P ojec adminis a ion, Funding acquisi ion, Concep ualiza ion. Decla a ion o compe ing in e es The au ho s decla e ha hey ha e no known compe ing inancial in e es s o pe sonal ela ionships ha could ha e appea ed o in luence he wo k epo ed in his pape . The i s au ho s a e Ph.D. candida es, and all ha dwa e componen s used in he s udy we e Fig. 11. Powe spec a diag am o he a e aged da a o bo h eyes open and eyes closed condi ions and S anda d on he op, and D eamMachine EEG sys ems on he bo om. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 23 pu chased wi h unding om he SIDDATA p ojec . 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Pa ia Samimisabe She is cu en ly pu suing a Ph.D. in Cogni i e Science a Osnab ück Uni e si y, wi h o e se en yea s o expe ience in PCB boa d design and EEG signal analysis. He esea ch has signi ican ly ad anced he ield, as e idenced by he p e ious publica ion, “ In oducing a New Mobile Elec oencephalog aphy Sys em and E alua ing I s Quali y in Compa ison o Clinical Elec oencephalog aphy ” This wo k ocused on he de elopmen and quali y assessmen o a no el mobile EEG (D eamMachine) sys em. Du ing he doc o al s udies, she designed an inno a i e mobile EEG de ice accompanied by a dedica ed And oid applica ion. This g oundb eaking de ice demons a es e sa ili y and po en ial applica ions ac oss a ious domains, including medical diagnos ics, educa ional ools, and esea ch ini ia i es. Lau a K iege is cu en ly pu suing a Ph.D. in Cogni i e Science a Osnab ück Uni e si y. He esea ch ocuses on de eloping ad anced Deep Lea ning a chi ec u es. Th ough he wo k on he D eamMachine p ojec , she aims o enable use s o pe o m high-quali y EEG eco dings, e en in he absence o eliable medical in as uc u e. Ma c Vidal de Palol is a Ph.D. candida e in Cogni i e Science a Osnab ück Uni e si y. His esea ch ocuses on ask-d i en beha io and isual a en ion in ealis ic i ual eali y (VR) en i onmen s, explo ing how imme sion and con ex in luence pe cep ion, a en ion, and memo y. Using eye- acking and EEG, he in es iga es cogni i e p ocesses by analyzing gaze poin s and b ain ac i i y du ing VR asks, such as simula ed ca ides in i ual ci ies. Ma c is also he one o he c ea o o he EEGD oid applica ion, designed o he D eamMachine, enhancing EEG echnology applica ions. P. Samimisabe e al. Ha dwa eX 23 (2025) e00689 25