Transfer approach for the detection of missed task-relevant events in P300-based brain-computer interfaces

Abstract :

Detection of human cognitive states using biosignals such as the electroencephalogram (EEG) is gaining relevance in different application areas, e.g., teleoperation, humanrobot collaboration, and rehabilitation. Especially, the P300, which is evoked as an event-related potential (ERP), when humans perceive task-relevant infrequent events among taskirrelevant frequent events, is widely used in brain-computer interfaces (BCIs). P300 detection has been used as an indicator that a human perceives task-relevant events or detects the occurrence of task-relevant or important events. In this paper, we focus on not only perceived task-relevant events but also not-perceived task-relevant events (i.e., missed events). In fact, a human can miss task-relevant events for different reasons, e.g., reduced attention level or increased workload level during parallel task-processing situations among others. Moreover, a human can also intentionally ignore task-relevant events to manage several simultaneous tasks. However, such missed events do not often occur in real-world applications. In this paper, we propose a transfer approach to handle insufficient number of events for training a classifier. For example, taskirrelevant infrequent events are used for training of classifier to detect missed task-relevant events. We evaluated our approach in different settings of training and testing a classifier with and without classifier transfer.

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Publikation_-_Transfer_approach_for_the_detection_of_missed_task-relevant_events_in_P300-based_brain-computer_interfaces.pdf