G. Pfurtscheller et al., ONLINE EEG CLASSIFICATION DURING EXTERNALLY-PACED HAND MOVEMENTS USING A NEURAL-NETWORK-BASED CLASSIFIER, Electroencephalography and clinical neurophysiology, 99(5), 1996, pp. 416-425
EEGs of 6 normal subjects were recorded during sequences of periodic l
eft or right hand movement. Left or right was indicated by a visual cu
e. The question posed was: 'Is it possible to move a cursor on a monit
or to the right or left side using the EEG signals for cursor control?
' For this purpose the EEG during performance of hand movement was ana
lyzed and classified on-line. a neural network in form of a learning v
ector quantizertion (LVQ) with an input dimension of 16 was trained to
classify EEG patterns from two electrodes and two time windows. After
two training session on 2 different days, 4 subjects showed a classif
ication accuracy of 89-100%. For two subjects classification was not p
ossible. These results show that in general movement specific EEG-patt
erns can be found, classified in real time and used to move a cursor o
n a monitor to the left or right. On-line EEG classification is necess
ary when the EEG is used as input signal to a brain computer interface
(BCI). Such a BCI can be a help for handicapped people.