Transfer approach for the detection of missed task-relevant events in P300-based brain-computer interfaces
Elsa Andrea Kirchner, Su-Kyoung Kim
In Proceedings in the 9th International IEEE EMBS Conference On Neural Engineering (NER’19), (NER-2019), 20.3.-23.3.2019, San Francisco, CA, IEEE Xplore, pages 134-138, 2019.
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.