Khan, Ozair Idris Farooq, Faisal Akram, Faraz Choi, Mun-Taek Han, Seung Moo Kim, Tae-Seong 2024-01-20T15:04:52Z 2024-01-20T15:04:52Z 2022-01-25 2012-03 0140-0118 https://pubs.kist.re.kr/handle/201004/129438 P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better. English SPRINGER HEIDELBERG Robust extraction of P300 using constrained ICA for BCI applications Article 10.1007/s11517-012-0861-4 1 MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, v.50, no.3, pp.231 - 241 MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 50 3 231 241 N scie scopus 000302263100004 2-s2.0-84862817582 Computer Science, Interdisciplinary Applications Engineering, Biomedical Mathematical & Computational Biology Medical Informatics Computer Science Engineering Mathematical & Computational Biology Medical Informatics Article INDEPENDENT COMPONENT ANALYSIS MENTAL PROSTHESIS BRAIN DISCRIMINATION COMMUNICATION MOVEMENT Brain computer interface P300 extraction Independent component analysis Constrained ICA