OBJECTIVES: We studied the activation of cortical motor areas during a memorized delay task with a classification technique. METHODS: Multichannel EEG was recorded during the sequence of warning stimulus, visual cue, reaction stimulus, and actual execution of hand or foot movements. Two different approaches are presented: first, we trained a classifier on data from the time segments immediately preceding the actual movements, and analyzed the whole recordings in overlapping segments with this fixed classifier. The classification rates obtained as a function of experimental time reflect the activation of the same cortical areas that are active during the actual movements. In the second approach, we trained classifiers on data segments with the same latency in time as the data tested ('running classifiers'). By this, we checked whether we could detect event-related activity sufficiently marked to allow for correct classification. RESULTS: With the fixed classifier approach we found two maxima of classification: one maximum after processing of the visual cue corresponding to an activation of motor cortex without overt movement, and a second maximum at the time of the actual movement. The first maximum relates to a very short-lived brain state, in the order of 300 ms, while the broad second maximum (1.5 s) indicates a very stable and long-lasting activation. CONCLUSIONS: With the running classifier approach we found similar maxima as with the fixed classifier, indicating that only the activity of motor areas is relevant for classification. Possible implications of our findings for the development of a brain computer interface (BCI) are discussed.
OBJECTIVES: We studied the activation of cortical motor areas during a memorized delay task with a classification technique. METHODS: Multichannel EEG was recorded during the sequence of warning stimulus, visual cue, reaction stimulus, and actual execution of hand or foot movements. Two different approaches are presented: first, we trained a classifier on data from the time segments immediately preceding the actual movements, and analyzed the whole recordings in overlapping segments with this fixed classifier. The classification rates obtained as a function of experimental time reflect the activation of the same cortical areas that are active during the actual movements. In the second approach, we trained classifiers on data segments with the same latency in time as the data tested ('running classifiers'). By this, we checked whether we could detect event-related activity sufficiently marked to allow for correct classification. RESULTS: With the fixed classifier approach we found two maxima of classification: one maximum after processing of the visual cue corresponding to an activation of motor cortex without overt movement, and a second maximum at the time of the actual movement. The first maximum relates to a very short-lived brain state, in the order of 300 ms, while the broad second maximum (1.5 s) indicates a very stable and long-lasting activation. CONCLUSIONS: With the running classifier approach we found similar maxima as with the fixed classifier, indicating that only the activity of motor areas is relevant for classification. Possible implications of our findings for the development of a brain computer interface (BCI) are discussed.
Authors: Clemens Brunner; Brendan Z Allison; Dean J Krusienski; Vera Kaiser; Gernot R Müller-Putz; Gert Pfurtscheller; Christa Neuper Journal: J Neurosci Methods Date: 2010-02-11 Impact factor: 2.390
Authors: Charles Aissani; Jacques Martinerie; Lydia Yahia-Cherif; Anne-Lise Paradis; Jean Lorenceau Journal: PLoS One Date: 2014-04-29 Impact factor: 3.240