The DOI matches its Crossref record; 151 of the 155 references it cites were found in a registry or an index.
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology
Open accessAbstract
Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Yaşlı Kadınlarda Üreme Sağlığı
Kokanalı, D., & Engin Üstün, Y. (n.d.) · DergiPark (Istanbul University) · Cited by 170,090
Two new Later Stone Age sites from the Final Pleistocene in the Falémé Valley, eastern Senegal
Ndiaye, M., Lespez, L., Tribolo, C., Rassé, M., Hadjas, I., Davidoux, S., Huysecom, É., & Douze, K. (2024) · Monash University Research Portal (Monash University) · doi:10.3929/ethz-b-000667478 · Cited by 24,214
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis
Klimesch, W. (1999) · Brain Research Reviews · doi:10.1016/s0165-0173(98)00056-3 · Cited by 6,889
Actual causes of death in the United States
McGinnis, J. M. (1993) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1993.03510180077038 · Cited by 5,480
Actual Causes of Death in the United States, 2000
Mokdad, A. H. (2004) · JAMA · doi:10.1001/jama.291.10.1238 · Cited by 5,211
Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential
Leski, S., Lindén, H., Tetzlaff, T., Pettersen, K. H., & Einevoll, G. T. (2013) · PubMed · doi:10.18154/rwth-conv-084442 · Cited by 3,667
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
Research works printed but not linked: 5
- Kübler A. Furdea A. Halder S. Hösle A. (2008). “Brain painting – BCI meets art,” in Proceedings of the 4th International Brain–Computer Interface Workshop and Training Course 2008, eds Müller-Putz G. R. Brunner C. Leeb R. Pfurtscheller G. Neuper C. (Graz: Verlag der Technischen Universität), 361– 366
- Maeder C. Sannelli C. Haufe S. Lemm S. Blankertz B. (2010). “Effect of prestimulus SMR amplitude on BCI performance,” in Poster at the TOBI Workshop ‘Integrating Brain–Computer Interfaces with Conventional Assistive Technology’, Graz.
- Sturm I. Curio G. Blankertz B. (2010). “Single-trial ERP analysis reveals unconsciously perceived structures of music,” in Poster at the TOBI Workshop ‘Integrating Brain–Computer Interfaces with Conventional Assistive Technology, Graz
- Tangermann M. Krauledat M. Grzeska K. Sagebaum M. Blankertz B. Vidaurre C. Müller K.-R. (2009). Playing Pinball with Non-Invasive BCI, in Advances in Neural Information Processing Systems, Vol. 21 (Cambridge, MA: MIT Press), 1641– 1648
- Vidaurre C. Sannelli C. Müller K.-R. Blankertz B. (2010). Machine-learning based Co-adaptive calibration. Neural Comput. (in press). 21162666
Cited by
Sorted by citations
Items 301–353 of 353
Controlling Decisions by Head Electrical Signals
Zero, E., Bozzi, A., Graffione, S., & Sacile, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-16281-7_40 · Cited by 1
Multi-input CNN Based Classification of EEG and NIRS Signal During Voluntary Hand Movement
Leelasiri, P., Takahashi, R., Aki, F., Ohshima, H., & YAMAZAKI, K. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-61628-0_16 · Cited by 1
Further Life Science Applications
Dickhaus, T. (2014) · Springer eBooks · doi:10.1007/978-3-642-45182-9_12 · Cited by 1
Skilled Action and the Ethics of Brain-Computer Interfaces
Drosselmeier, S., & Sellmaier, S. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-64590-8_3 · Cited by 1
Adaptive Boosting of DNN Ensembles for Brain-Computer Interface Spellers
Güney, O. B., Koç, E., Aksoy, C., Çatak, Y., Arslan, Ş. S., & Özkan, H. (2021) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu53274.2021.9477841 · Cited by 1
Effect of Hand Dominance When Decoding Motor Imagery Grasping Tasks
Nergård, K. L., Endestad, T., & Tørresen, J. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-08443-0_15 · Cited by 1
Alonso, N., & Fernando, L. (2012) · UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) · Cited by 1
Predictive modeling using sparse logistic regression with applications
Manninen, T. (2014) · Tampere University Institutional Repository (Tampere University) · Cited by 1
Rezazadeh, A. (2018) · TSpace (University of Toronto) · Cited by 1
Noninvasive Brain-Computer Interfaces
Allison, B. Z., & Krusienski, D. J. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6675-8_707 · Cited by 1
Niveaux d'automatisation adaptables pour une coopération homme-robots
Habib, L. (2019) · doi:10.70675/61fe21a5z7e1fz442cza48bzac1a19d65a16 · Cited by 1
Combining Occupancy Grids with a Polygonal Obstacle World Model for Autonomous Flights
Andert, F., & Goorm, L. (2009) · InTech eBooks · doi:10.5772/6465
Integración de información en tareas secuenciales
Kamienkowski, J. E. (2011) · Repositorio Digital Institucional de la Universidad de Buenos Aires (Universidad de Buenos Aires)
Basul, I. A. (2016) · Experimental Psychology (Russia) · doi:10.17759/exppsy.2015080410
The Collective Experience of Empathic Data Systems
Bernardet, U., Makridis, M., Daras, P., Olbrich, E., & Pizzi, D. (2011)
Platformy edukacyjne z neuronowym sprzężeniem zwrotnym – zestaw badawczy
Środowiskowe Studium Doktoranckie IPPT PAN w Warszawie i UMCS w Lublinie, O. P. 2., Golus, W., Łojewski, Z., & Zakład Informatyki Stosowanej, W. M. (2015) · Nierówności społeczne a wzrost gospodarczy · doi:10.15584/nsawg.2015.4.1.11
Rosa, J. L. G. (2016) · doi:10.5753/sbc.6.5
Designing a hands-on brain computer interface laboratory course
Khalighinejad, B., Long, L., & Mesgarani, N. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591363
Quality assessment of 3D visualizations with vertical disparity: An ERP approach
Avarvand, F. S., Bosse, S., Nolte, G., Wiegand, T., & Samek, W. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037829
Brain-computer interface for mental attention
Fahimi, F. (2019) · doi:10.32657/10356/137001
Investigation of EEG Correlate in NIRS Signal for BCI
Johar, A. H., Yousaf, T., Asgher, U., Ayaz, Y., Nazir, S., Khan, M. J., Mustafa, L., & Khalil, K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51041-1_42
Inter-subject correlations during natural viewing: A filter-bank approach
Gait, A., Duisenbinov, V., Lee, M.-H., Biessmann, F., & Fazli, S. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176083
Self-citationZerafa, R., Camilleri, T. A., Falzon, O., & Camilleri, K. P. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-72379-8_13
Utilizing Machine Learning and Virtual Reality to Facilitate Brain-computer Interface Control
Reyhani-Masoleh, B. (2019) · TSpace (University of Toronto)
Classification of Motor Imagery Tasks for Brain-Computer Interface using EEG Signals
Maruf, L., Alam, Z., Rahman, M. M.-E.-N., Rahman, A., Hough, P., & Parvez, M. Z. (2020) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde50874.2020.9411545
Introducing social networks and brain computer interaction
Bourke, S., Healy, G. F., Smeaton, A. F., & Smyth, B. (2012) · Research Repository UCD (University College Dublin)
Towards Context-aware Brain-computer Interfaces
Myrden, A. (2016) · TSpace (University of Toronto)
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural NetworksPreprint
Ahn, H.-J., Lee, D.-H., Jeong, J.-H., & Lee, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07335
(2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801543-8.00019-3
Jornadas de Atualização em Informática 2016
Maldonado, J. C., Viterbo, J., Delamaro, M. E., Tede, P. C. D. A. R., Kon, F., Zambom, E., Silva, T. H., Loureiro, A. A. F., de Figueiredo, C. M. H., Marczak, S. D. S., et al. (2016) · doi:10.5753/sbc.6
Borghini, G., Aricò, P., Flumeri, G. D., & Babiloni, F. (2017) · Biosystems & biorobotics · doi:10.1007/978-3-319-58598-7_1
Noninvasive Brain-Computer Interfaces
Allison, B. Z., & Krusienski, D. J. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_707
A Study on Electrode Positions Around the Ear for BCI Development using SSVEP
Ito, M., Cui, G., Kinoshita, F., & Touyama, H. (2023) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.143.178
Xu, Y., Yi, W., Yin, H., & Peng, Y. (2022) · International Conference on Information Systems and Computing Technology (ISCTech) · doi:10.1109/isctech58360.2022.00068
Vishnupriya, R., Robinson, N., & Reddy, M. R. (2023) · Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · doi:10.11159/icbes23.126
Multimodal recognition of speech and electrocorticogram
Ahuja, M., Komeiji, S., Mitsuhashi, T., Iimura, Y., Suzuki, H., Sugano, H., Shinoda, K., & Tanaka, T. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317527
Nöropsikoloji ve Nöropsikolojik Psikoterapi
Özen, Y. (2024) · Akademisyen Kitabevi eBooks · doi:10.37609/akya.3077
Mental Work Load Assessment Using Brain Connectivity Based Features of Simultaneous EEG and fNIRS
Qaremohammadlou, M., & Shamsollahi, M. B. (2024) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme64381.2024.10895072
EEG Signal Classification Reinforced by LDConv and Transformer
Yue, Y., Liang, X., Tong, J., Yang, S., Chang, Y., & Du, S. (2025) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma65362.2025.11120669
Zhou, Q., Zhao, Y., Ye, X., Song, J., Du, Q., & Li, K. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227880
Sengupta, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3922082
Alzahrani, S., Banjar, H. R., & Mirza, R. (2025) · IEEE Access · doi:10.1109/access.2025.3640637
Amarjeet, Tiwari, N., & Anwar, S. (2025) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon67893.2025.11327384
Chen, Y., Kang, T., & Wallraven, C. (2026) · Biological Psychology · doi:10.1016/j.biopsycho.2026.109233
Towards Neuroadaptive Technology: Ongoing reflections on Project NAFAS
Krol, L. R., Pesch, M., & Zander, T. O. (2026) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci69045.2026.11435048
Optimizing the use of SSVEP-based brain-computer interfaces for human-computer interaction
Évain, A. (2016) · theses.fr (ABES) · doi:10.70675/a2acdacdz2271z4334z9e1dz869ee8760420 · Cited by 1
User Adapted Brain-Computer Interface
Turi, F. (2020) · theses.fr (ABES) · doi:10.70675/1cc7d8e9z0922z4628za8c6zd476ec105ae4
Bontonou, M. (2021) · theses.fr (ABES) · doi:10.70675/4e46bed6zde51z4571z94caz198d67555e82
胡, 皓. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1641751
Kim, D., Bak, S., Kim, S., & Shin, J. (2026) · Sensors · doi:10.3390/s26103097
Yuan, X., Meng, Q., Bao, C., Feng, X., & Xiao, M. (2026) · ACS Omega · doi:10.1021/acsomega.6c02929
Metadata
- Authors
- Blankertz, B., Tangermann, M., Vidaurre, C., Fazli, S., Sannelli, C., Haufe, S., Maeder, C., Ramsey, L., Sturm, I., Curio, G., & Müller, K.-R.
- Affiliations
- Fraunhofer Institute for Open Communication Systems (DE)
- Technische Universität Berlin (DE)
- Utrecht University (NL)
- Charité – Universitätsmedizin Berlin (DE)
- Corresponding author
- Blankertz, Benjamin
- ORCID
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2010
- Volume
- 4
- Pages
- 198-198
- ISSN
- 1662-453X (ISSN-L)
- 1662-4548
- PMC
- PMC3002462
- OpenAlex
- W1969715890
- Cited by
- 357
- References
- 155
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 13 · 2023: 20 · 2022: 18 · 2021: 28 · 2020: 30 · 2019: 20 · 2018: 32 · 2017: 32 · 2016: 32 · 2015: 22 · 2014: 33 · 2013: 24 · 2012: 14
- FWCI
- 10.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- brain-computer interface, mental state monitoring, healthy volunteers, usability, ease of use, control latency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
