Objective: We investigated the efficacy of a P300-based brain-computer interface (BCI) for patients with spinocerebellar ataxia (SCA), which is often accompanied by cerebellar impairment.
Methods: Eight patients with SCA and eight age- and gender-matched healthy controls were instructed to input Japanese hiragana characters using the P300-based BCI with green/blue flicker. All patients depended on some assistance in their daily lives (modified Rankin scale: mean 3.5). The chief symptom was cerebellar ataxia; no cognitive deterioration was present. A region-based, two-step P300-based BCI was used. During the P300 task, eight-channel EEG data were recorded, and a linear discriminant analysis distinguished the target from other nontarget regions of the matrix.
Results: The mean online accuracy in BCI operation was 82.9% for patients with SCA and 83.2% for controls; no significant difference was detected.
Conclusion: The P300-based BCI was operated successfully not only by healthy controls but also by individuals with SCA.
Significance: These results suggest that the P300-based BCI may be applicable for patients with SCA.
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http://dx.doi.org/10.1016/j.cnp.2017.06.004 | DOI Listing |
J Neural Eng
December 2024
Ulsan National Institute of Science and Technology, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, Republic of Korea, Ulsan, 44919, Korea (the Republic of).
Objective: In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands.
Approach: In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses.
Brain Sci
August 2024
Centro de Investigación en Informática Aplicada (CIDIA), Universidad Nacional de Hurlingham (UNAHUR), Hurlingham B1688, Argentina.
This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision.
View Article and Find Full Text PDFFront Neurosci
July 2024
Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands.
Introduction: Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples.
View Article and Find Full Text PDFJ Neural Eng
July 2024
Insight SFI Research Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland.
Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP.
View Article and Find Full Text PDFFront Hum Neurosci
March 2024
Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany.
Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control.
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