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Shojaedini, Seyed Vahab, Morabbi, Sajedeh. (1398). Sparse Representation-Based Classification (SRC): A Novel Method to Improve the Performance of Convolutional Neural Networks in Detecting P300 Signals. سامانه مدیریت نشریات علمی, 6(2), 37-46.
Seyed Vahab Shojaedini; Sajedeh Morabbi. "Sparse Representation-Based Classification (SRC): A Novel Method to Improve the Performance of Convolutional Neural Networks in Detecting P300 Signals". سامانه مدیریت نشریات علمی, 6, 2, 1398, 37-46.
Shojaedini, Seyed Vahab, Morabbi, Sajedeh. (1398). 'Sparse Representation-Based Classification (SRC): A Novel Method to Improve the Performance of Convolutional Neural Networks in Detecting P300 Signals', سامانه مدیریت نشریات علمی, 6(2), pp. 37-46.
Shojaedini, Seyed Vahab, Morabbi, Sajedeh. Sparse Representation-Based Classification (SRC): A Novel Method to Improve the Performance of Convolutional Neural Networks in Detecting P300 Signals. سامانه مدیریت نشریات علمی, 1398; 6(2): 37-46.

Sparse Representation-Based Classification (SRC): A Novel Method to Improve the Performance of Convolutional Neural Networks in Detecting P300 Signals

Health Management & Information Science
مقاله 1، دوره 6، شماره 2، تیر 2019، صفحه 37-46    اصل مقاله (694.52 K)
نوع مقاله: Articles
نویسندگان
Seyed Vahab Shojaedini* ؛ Sajedeh Morabbi
چکیده
Introduction: Brain-Computer Interface (BCI) offers a non-muscle way between the humanbrain and the outside world to make a better life for disabled people. In BCI applicationsP300 signal has an effective role; therefore, distinguishing P300 and non-P300 componentsin EEG signal (i.e. P300 detection) becomes a vital problem in BCI applications. Recently,Convolutional Neural Networks (CNNs) have had a significant application in detection ofP300 signals in the field of BCIs. The P300 signal has low Signal to Noise Ratio (SNR). Onthe other hand, the CNN detection rate is so sensitive to SNR; therefore, CNN detection ratedrops dramatically when it is faces with P300 data. In this study, a novel structure is proposed to improve the performance of CNN in P300 signal detection by means of improving its performance against low SNR signals.Methods: In the proposed structure, Sparse Representation-based Classification (SRC) wasused as the first substructure. This block is responsible for prediction of the expected P300signal among artifacts and noise. The second substructure performed P300 classification with Adadelta algorithm. Thanks to such SNR improvement scheme; the proposed structure i able to increase the rate of accuracy in the field of P300 signal detection.Results: To evaluate the performance of the proposed structure, we applied it on EPFLdataset for P300 detection, and then the achieved results were compared with those obtained from the basic CNN structure. The comparisons revealed the superiority of the proposed structure against its alternative, so that its True Positive Rate (TPR) was promoted about 19.66%. Such improvements for false detections and accuracy parameters were 1.93% and 10.46%, respectively, which show the effectiveness of applying the proposed structure in detecting P300 signals.Conclusion: The better accuracy of the proposed algorithm compared to basic CNN, inparallel with its more robustness, showed that the Sparse Representation-based Classification (SRC) had a considerable potential to be used as an improving idea in CNN-based P300 detection.Keywords: EEG, Neural Networks, Signal Detection, Machine Learning, Brain-ComputerInterfaces, Brain-Computer Interface, Brain, Neuroscience, P300, Convolutional NeuralNetworks, Deep Learning
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