A cognitive brain-computer interface prototype for the continuous monitoring of visual working memory load
2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)(2015)
摘要
We investigate the neural correlates of visual working memory using electroencephalography (EEG). Our objective is to develop a cognitive Brain-Computer Interface (BCI) able to monitor visual working memory load in real-time. A system with these properties would eventually have different applications, such as training, rehabilitation, or safety while operating dangerous machinery. The BCI performances were evaluated using cross-validation. With an appropriately chosen classification threshold, it is possible to detect high working memory load with a sensitivity of 68% and a specificity of 72%. However, it is well known that some subjects are BCI illiterate, meaning that up to 30% of the users have too high signal variability to use EEG-based BCI systems. If we analyse each subject individually, it is possible to detect high working memory load with a sensitivity of 78% and a specificity of 81% (accuracy = 81%) for a typical good subject. Changes due to visual working memory load were observed in frontal, parietal, and occipital regions.
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关键词
Brain-computer interfaces,cognitive information processing,pattern recognition,classification
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