Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.
|アブストラクト||Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, non-invasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance deduction are proposed.|
|投稿者||Thompson, David E; Mowla, Md Rakibul; Dhuyvetter, Katie J; Tillman, Joseph W; Huggins, Jane E|
|ジャーナル名||Brain computer interfaces (Abingdon, England)|
|組織名||Brain and Body Sensing (BBS) Lab, Mike Wiegers Department of Electrical and;Computer Engineering, Kansas State University, Manhattan, KS, USA.;Direct Brain Interface Lab, Department of Physical Medicine and Rehabilitation;and Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI,;USA.|