Asynchronous Control of P300-Based Brain–Computer Interfaces Using Sample Entropy

• Published in: Entropy (Basel, Switzerland) Vol. 21; no. 3; p. 230
• Main Authors: Martínez-Cagigal, Víctor, Santamaría-Vázquez, Eduardo, Hornero, Roberto
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.02.2019; MDPI
• Subjects: Algorithms; asynchrony; brain–computer interfaces; Datasets; Discriminant analysis; Electrodes; Electroencephalography; Entropy; event-related potentials; Human-computer interface; multiscale entropy; oddball paradigm; Optimization; P300-evoked potentials; sample entropy; P300-evoked potentials; multiscale entropy; sample entropy; asynchrony; brain–computer interfaces; event-related potentials; oddball paradigm
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e21030230

Brain–computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, to the best of our knowledge, entropy metrics have not yet been explored. The present study has a twofold purpose: (i) to characterize both control and non-control states by examining the regularity of electroencephalography (EEG) signals; and (ii) to assess the efficacy of a scaled version of the sample entropy algorithm to provide asynchronous control for BCI systems. Ten healthy subjects participated in the study, who were asked to spell words through a visual oddball-based paradigm, attending (i.e., control) and ignoring (i.e., non-control) the stimuli. An optimization stage was performed for determining a common combination of hyperparameters for all subjects. Afterwards, these values were used to discern between both states using a linear classifier. Results show that control signals are more complex and irregular than non-control ones, reaching an average accuracy of 94 . 40 % in classification. In conclusion, the present study demonstrates that the proposed framework is useful in monitoring the attention of a user, and granting the asynchrony of the BCI system.