Skip to main content
Top

2020 | OriginalPaper | Chapter

Recent Trends and Open Challenges in EEG Based Brain-Computer Interface Systems

Authors : Mamunur Rashid, Norizam Sulaiman, Mahfuzah Mustafa, Sabira Khatun, Bifta Sama Bari, Md Jahid Hasan

Published in: InECCE2019

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Recent advances in computer hardware and signal processing have made possible the use of electroencephalogram (EEG) for communication between human brain and computers and this technology is known as brain-computer interface (BCI). Locked-in patients have now a way to communicate with the outside world using BCI technology. Nowadays, BCIs are getting popularity among the researchers to control devices using brainwaves especially in providing good assistance to disabled people. Impressive development and integration of both hardware and software in BCI have been carried out in the last two decades. However, some open challenges and limitations have also been exposed in the previous researches. In this paper, we have tried to mention some critical issues of EEG based BCI system including EEG modalities, EEG acquisition, signal processing algorithm and performance evaluation. These issues need to be solved to develop error-free BCI system. In addition, possible solutions and future directions have also been discussed.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Birbaumer N (2006) Breaking the silence: brain? Computer interfaces (BCI) for communication and motor control. Psychophysiology 43:517–532CrossRef Birbaumer N (2006) Breaking the silence: brain? Computer interfaces (BCI) for communication and motor control. Psychophysiology 43:517–532CrossRef
2.
go back to reference Wang X-Y, Jin J, Zhang Y, Wang B (2014) Brain control: human-computer integration control based on brain-computer interface. Acta Autom Sin 39:208–221CrossRef Wang X-Y, Jin J, Zhang Y, Wang B (2014) Brain control: human-computer integration control based on brain-computer interface. Acta Autom Sin 39:208–221CrossRef
3.
go back to reference Nicolas-Alonso LF, Gomez-Gil J Brain computer interfaces, a review Nicolas-Alonso LF, Gomez-Gil J Brain computer interfaces, a review
4.
go back to reference Abdulkader SN, Atia A, Mostafa MSM (2015) Brain computer interfacing: applications and challenges. Egypt. Informatics J. 16:213–230CrossRef Abdulkader SN, Atia A, Mostafa MSM (2015) Brain computer interfacing: applications and challenges. Egypt. Informatics J. 16:213–230CrossRef
5.
go back to reference Ramadan RA, Vasilakos AV (2017) Brain computer interface: control signals review. Neurocomputing Ramadan RA, Vasilakos AV (2017) Brain computer interface: control signals review. Neurocomputing
6.
go back to reference Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X (2018) A comprehensive review of EEG-based brain-computer interface paradigms. J Neural Eng Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X (2018) A comprehensive review of EEG-based brain-computer interface paradigms. J Neural Eng
7.
go back to reference Stamatto Ferreira AL, Cunha de Miranda L, Cunha de Miranda EE, Gomes Sakamoto S A survey of interactive systems based on brain-computer interfaces Stamatto Ferreira AL, Cunha de Miranda L, Cunha de Miranda EE, Gomes Sakamoto S A survey of interactive systems based on brain-computer interfaces
8.
go back to reference He B, Gao S, Yuan H, Wolpaw JR Brain-computer interfaces He B, Gao S, Yuan H, Wolpaw JR Brain-computer interfaces
9.
go back to reference Khorshidtalab A, Salami MJE (2011) EEG signal classification for real-time brain-computer interface applications: a review. In: 2011 4th international conference on mechatronics: integrated engineering for industrial and societal development, ICOM’11—conference proceedings Khorshidtalab A, Salami MJE (2011) EEG signal classification for real-time brain-computer interface applications: a review. In: 2011 4th international conference on mechatronics: integrated engineering for industrial and societal development, ICOM’11—conference proceedings
10.
go back to reference Zaizu Ilyas M, Saad P, Imran Ahmad M (2015) A survey of analysis and classification of EEG signals for brain-computer interfaces. In: 2nd international conference on biomedical engineering (ICoBE). Penang, Malaysia, pp 1–6 Zaizu Ilyas M, Saad P, Imran Ahmad M (2015) A survey of analysis and classification of EEG signals for brain-computer interfaces. In: 2nd international conference on biomedical engineering (ICoBE). Penang, Malaysia, pp 1–6
11.
go back to reference Muller-Putz GR, Scherer R, Neuper C, Pfurtscheller G (2006) Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? IEEE Trans Neural Syst Rehabil Eng 14:30–37CrossRef Muller-Putz GR, Scherer R, Neuper C, Pfurtscheller G (2006) Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? IEEE Trans Neural Syst Rehabil Eng 14:30–37CrossRef
12.
go back to reference Polich J, Ellerson PC, Cohen J (1996) P300, stimulus intensity, modality, and probability. Int J Psychophysiol 23:55–62CrossRef Polich J, Ellerson PC, Cohen J (1996) P300, stimulus intensity, modality, and probability. Int J Psychophysiol 23:55–62CrossRef
13.
go back to reference Ravden D, Polich J (1999) On P300 measurement stability: habituation, intra-trial block variation, and ultradian rhythms. Biol Psychol 51:59–76CrossRef Ravden D, Polich J (1999) On P300 measurement stability: habituation, intra-trial block variation, and ultradian rhythms. Biol Psychol 51:59–76CrossRef
14.
go back to reference Rivet B, Souloumiac A, Attina V, Gibert G (2009) 2009_B.Rivet_xDAWN algorithm to enhance evoked potentials; application to brain–computer interface. IEEE Trans Bio Eng 56:2035–2043CrossRef Rivet B, Souloumiac A, Attina V, Gibert G (2009) 2009_B.Rivet_xDAWN algorithm to enhance evoked potentials; application to brain–computer interface. IEEE Trans Bio Eng 56:2035–2043CrossRef
15.
go back to reference Golub MD, Chase SM, Batista AP, Byron MY (2016) Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr Opin Neurobiol 37:53–58CrossRef Golub MD, Chase SM, Batista AP, Byron MY (2016) Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control. Curr Opin Neurobiol 37:53–58CrossRef
16.
go back to reference Phothisonothai M, Nakagawa M (2008) EEG signal classification method based on fractal features and neural network. In: 2008 30th annual international conference of the IEEE engineering in medicine and biology society, pp 3880–3883 Phothisonothai M, Nakagawa M (2008) EEG signal classification method based on fractal features and neural network. In: 2008 30th annual international conference of the IEEE engineering in medicine and biology society, pp 3880–3883
17.
go back to reference Rashid M, Sulaiman N, Mustafa M, Khatun S, Bari BS (2019) The classification of EEG signal using different machine learning techniques for BCI application. In: Jong-Hwan K, Kim Hyung Myung SML (eds) Robot intelligence technology and applications. RiTA 2018. Communications in computer and information science, vol 1015. Springer, Singapore, pp 207–221 Rashid M, Sulaiman N, Mustafa M, Khatun S, Bari BS (2019) The classification of EEG signal using different machine learning techniques for BCI application. In: Jong-Hwan K, Kim Hyung Myung SML (eds) Robot intelligence technology and applications. RiTA 2018. Communications in computer and information science, vol 1015. Springer, Singapore, pp 207–221
18.
go back to reference Lakshmi MR, Prasad TV, Chandra Prakash V (2014) Survey on EEG signal processing methods Lakshmi MR, Prasad TV, Chandra Prakash V (2014) Survey on EEG signal processing methods
19.
go back to reference Ghosh T, Science P, Biswas T, Science P (2016) A feature extraction scheme to classify motor imagery movements based on bi-spectrum analysis of EEG. IOSR J VLSI Sign Process 6:28–35 Ghosh T, Science P, Biswas T, Science P (2016) A feature extraction scheme to classify motor imagery movements based on bi-spectrum analysis of EEG. IOSR J VLSI Sign Process 6:28–35
20.
go back to reference Biswas T, Ahmad Fauzi MF, Abas FS, Nair HKR (2019) Superpixel classification with color and texture features for automated wound area segmentation. 2018 IEEE Student Conf Res Dev 1–6 Biswas T, Ahmad Fauzi MF, Abas FS, Nair HKR (2019) Superpixel classification with color and texture features for automated wound area segmentation. 2018 IEEE Student Conf Res Dev 1–6
21.
go back to reference Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4:R1–R13CrossRef Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4:R1–R13CrossRef
22.
go back to reference Rezeika A, Benda M, Stawicki P, Gembler F, Saboor A, Volosyak I (2018) Brain-computer interface spellers: a review. Brain Sci 8:57CrossRef Rezeika A, Benda M, Stawicki P, Gembler F, Saboor A, Volosyak I (2018) Brain-computer interface spellers: a review. Brain Sci 8:57CrossRef
23.
go back to reference Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A, Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A (2017) Review and classification of emotion recognition based on eeg brain-computer interface system research: a systematic review. Appl Sci 7:1239CrossRef Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A, Al-Nafjan A, Hosny M, Al-Ohali Y, Al-Wabil A (2017) Review and classification of emotion recognition based on eeg brain-computer interface system research: a systematic review. Appl Sci 7:1239CrossRef
24.
go back to reference Ma X, Liu Z, Jiang T, Zhang X (2019) Study of the algorithm for the classification of brain waves. In: Zhang QLMJWF (ed) Lecture notes in electrical engineering (LNEE, vol 463). Springer, Singapore, pp 2325–2331 Ma X, Liu Z, Jiang T, Zhang X (2019) Study of the algorithm for the classification of brain waves. In: Zhang QLMJWF (ed) Lecture notes in electrical engineering (LNEE, vol 463). Springer, Singapore, pp 2325–2331
25.
go back to reference Wali MK, Murugappan M, Badlishah Ahmad R Classification of driver drowsiness level using wireless EEG Wali MK, Murugappan M, Badlishah Ahmad R Classification of driver drowsiness level using wireless EEG
26.
go back to reference Fernández-Rodríguez Á, Velasco-Álvarez F, Ron-Angevin R (2016) Review of real brain-controlled wheelchairs. J Neural Eng 13:061001CrossRef Fernández-Rodríguez Á, Velasco-Álvarez F, Ron-Angevin R (2016) Review of real brain-controlled wheelchairs. J Neural Eng 13:061001CrossRef
27.
go back to reference Bousseta R, El Ouakouak I, Gharbi M, Regragui F (2018) EEG based brain computer interface for controlling a robot arm movement through thought. IRBM. 39:129–135CrossRef Bousseta R, El Ouakouak I, Gharbi M, Regragui F (2018) EEG based brain computer interface for controlling a robot arm movement through thought. IRBM. 39:129–135CrossRef
28.
go back to reference Zhang R, Wang Q, Li K, He S, Qin S, Feng Z, Chen Y, Song P, Yang T, Zhang Y, Yu Z, Hu Y, Shao M, Li Y (2017) A BCI-based environmental control system for patients with severe spinal cord injuries. IEEE Trans Biomed Eng 64:1959–1971CrossRef Zhang R, Wang Q, Li K, He S, Qin S, Feng Z, Chen Y, Song P, Yang T, Zhang Y, Yu Z, Hu Y, Shao M, Li Y (2017) A BCI-based environmental control system for patients with severe spinal cord injuries. IEEE Trans Biomed Eng 64:1959–1971CrossRef
29.
go back to reference Wu Q, Zeng Y, Zhang C, Tong L, Yan B (2018) An EEG-based person authentication system with open-set capability combining eye blinking signals. Sensors. 18:335CrossRef Wu Q, Zeng Y, Zhang C, Tong L, Yan B (2018) An EEG-based person authentication system with open-set capability combining eye blinking signals. Sensors. 18:335CrossRef
30.
go back to reference Singla R, Agrawal A, Kumar V, Verma OP (2018) Real-time mental workload detector for estimating human performance under workload. In: Karwal BSRTM (ed) Lecture notes in electrical engineering (LNEE, vol 526). Springer, Singapore, pp 383–392 Singla R, Agrawal A, Kumar V, Verma OP (2018) Real-time mental workload detector for estimating human performance under workload. In: Karwal BSRTM (ed) Lecture notes in electrical engineering (LNEE, vol 526). Springer, Singapore, pp 383–392
31.
go back to reference Nguyen P, Tran D, Huang X, Ma W (2013) Age and gender classification using EEG paralinguistic features. In: 2013 6th international IEEE/EMBS conference on neural engineering (NER). IEEE, pp 1295–1298 Nguyen P, Tran D, Huang X, Ma W (2013) Age and gender classification using EEG paralinguistic features. In: 2013 6th international IEEE/EMBS conference on neural engineering (NER). IEEE, pp 1295–1298
32.
go back to reference Bascil MS, Tesneli AY, Temurtas F (2016) Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN. Australas Phys Eng Sci Med 39:665–676CrossRef Bascil MS, Tesneli AY, Temurtas F (2016) Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN. Australas Phys Eng Sci Med 39:665–676CrossRef
33.
go back to reference Maby E, Perrin M, Bertrand O, Sanchez G, Mattout J (2012) BCI could make old two-player games even more fun: a proof of concept with “connect four. Adv Human-Comput Interact Maby E, Perrin M, Bertrand O, Sanchez G, Mattout J (2012) BCI could make old two-player games even more fun: a proof of concept with “connect four. Adv Human-Comput Interact
34.
go back to reference Aydin EA, Bay OF, Guler I (2018) P300-based asynchronous brain computer interface for environmental control system. IEEE J Biomed Heal Inform 22:653–663CrossRef Aydin EA, Bay OF, Guler I (2018) P300-based asynchronous brain computer interface for environmental control system. IEEE J Biomed Heal Inform 22:653–663CrossRef
36.
go back to reference Mara S, Müller T, Freire T, Mário B, Filho S (2013) Proposal of a SSVEP-BCI to command a robotic wheelchair. J Control Autom Electr Syst 24:97–105CrossRef Mara S, Müller T, Freire T, Mário B, Filho S (2013) Proposal of a SSVEP-BCI to command a robotic wheelchair. J Control Autom Electr Syst 24:97–105CrossRef
37.
go back to reference Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70:510–523CrossRef Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70:510–523CrossRef
38.
go back to reference Ansari IA, Singla R (2016) BCI: an optimised speller using SSVEP. Int J Biomed Eng Technol 22:31CrossRef Ansari IA, Singla R (2016) BCI: an optimised speller using SSVEP. Int J Biomed Eng Technol 22:31CrossRef
39.
go back to reference Wang X-W, Nie D, Lu B-L (2014) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129:94–106CrossRef Wang X-W, Nie D, Lu B-L (2014) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129:94–106CrossRef
40.
go back to reference Jatupaiboon N, Panngum S, Israsena P (2013) Emotion classification using minimal EEG channels and frequency bands. In: The 2013 10th international joint conference on computer science and software engineering (JCSSE). IEEE (2013), pp 21–24 Jatupaiboon N, Panngum S, Israsena P (2013) Emotion classification using minimal EEG channels and frequency bands. In: The 2013 10th international joint conference on computer science and software engineering (JCSSE). IEEE (2013), pp 21–24
41.
go back to reference Long Jinyi, Li Yuanqing, Tianyou Yu, Zhenghui Gu (2012) Target Selection with hybrid feature for BCI-based 2-D cursor control. IEEE Trans Biomed Eng 59:132–140CrossRef Long Jinyi, Li Yuanqing, Tianyou Yu, Zhenghui Gu (2012) Target Selection with hybrid feature for BCI-based 2-D cursor control. IEEE Trans Biomed Eng 59:132–140CrossRef
42.
go back to reference Bonnet L, Lotte F, Lécuyer A (2013) Two brains, one game: design and evaluation of a multiuser BCI video game based on motor imagery. IEEE Trans Comput Intell AI Games 5:185–198CrossRef Bonnet L, Lotte F, Lécuyer A (2013) Two brains, one game: design and evaluation of a multiuser BCI video game based on motor imagery. IEEE Trans Comput Intell AI Games 5:185–198CrossRef
43.
go back to reference Abiyev RH, Akkaya N, Aytac E, Günsel I, Çağman A (2016) Brain-computer interface for control of wheelchair using fuzzy neural networks. Biomed Res Int 2016:1–9CrossRef Abiyev RH, Akkaya N, Aytac E, Günsel I, Çağman A (2016) Brain-computer interface for control of wheelchair using fuzzy neural networks. Biomed Res Int 2016:1–9CrossRef
44.
go back to reference Wolpaw JR, Birbaumer N, Mcfarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control Wolpaw JR, Birbaumer N, Mcfarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control
45.
go back to reference Bin G, Gao X, Wang Y, Li Y, Hong B, Gao S (2011) A high-speed BCI based on code modulation. VEP J Neural Eng 8:025015CrossRef Bin G, Gao X, Wang Y, Li Y, Hong B, Gao S (2011) A high-speed BCI based on code modulation. VEP J Neural Eng 8:025015CrossRef
46.
go back to reference Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, Neuper C (2011) Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface. Med Biol Eng Comput 49:181–191CrossRef Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, Neuper C (2011) Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface. Med Biol Eng Comput 49:181–191CrossRef
47.
go back to reference Schreuder M, Höhne J, Blankertz B, Haufe S, Dickhaus T, Tangermann M (2013) Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. J Neural Eng 10:036025CrossRef Schreuder M, Höhne J, Blankertz B, Haufe S, Dickhaus T, Tangermann M (2013) Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. J Neural Eng 10:036025CrossRef
Metadata
Title
Recent Trends and Open Challenges in EEG Based Brain-Computer Interface Systems
Authors
Mamunur Rashid
Norizam Sulaiman
Mahfuzah Mustafa
Sabira Khatun
Bifta Sama Bari
Md Jahid Hasan
Copyright Year
2020
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-2317-5_31