Online Classification of Single EEG Trials During Finger Movements

• Published in: IEEE transactions on biomedical engineering Vol. 55; no. 2; pp. 713 - 720
• Main Authors: Lehtonen, J., Jylanki, P., Kauhanen, L., Sams, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Artificial Intelligence; Bit rate; Brain computer interfaces; Brain Mapping - methods; Brain-computer interface (BCI); Classification algorithms; Control systems; Data collection; Electroencephalography; Electroencephalography - methods; Evoked Potentials, Motor - physiology; Feedback; Female; Fingers; Fingers - physiology; Humans; Male; Movement - physiology; Online Systems; online training; Pattern Recognition, Automated - methods; Power engineering and energy; single-trial classification; Studies; Testing; Timing; User-Computer Interface
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2007.912653

Many offline studies have explored the feasibility of EEG potentials related to single limb movements for a brain-computer interface (BCI) control signal. However, only few functional online single-trial BCI systems have been reported. We investigated whether inexperienced subjects could control a BCI accurately by means of visually-cued left versus right index finger movements, performed every 2 s, after only a 20-min training period. Ten subjects tried to move a circle from the center to a target location at the left or right side of the computer screen by moving their left or right index finger. The classifier was updated after each trial using the correct class labels, enabling up-to-date feedback to the subjects throughout the training. Therefore, a separate data collection session for optimizing the classification algorithm was not needed. When the performance of the BCI was tested, the classifier was not updated. Seven of the ten subjects were able to control the BCI well. They could choose the correct target in 84%-100% of the cases, 3.5-7.7 times a minute. Their mean single trial classification rate was 80% and bit rate 10 bits/min. These results encourage the development of BCIs for paralyzed persons based on detection of single-trial movement attempts.