Klinische Neurophysiologie 2004; 35 - 169
DOI: 10.1055/s-2004-832081

The Influence of Preceding Movements on Motor Cortical Activity in Finger-Tapping

F Losch 1, B Blankertz 2, KR Müller 3, G Curio 4
  • 1Berlin
  • 2Berlin
  • 3Berlin
  • 4Berlin

Introduction: Brain-computer interfaces (BCI) enable the control of external devices via EEG signals, possibly substituting the brain's normal output pathways in paralysed patients. One key problem is the still limited information transfer. This could be improved by increasing the rate of BCI commands; however, a faster command rate might be restricted eventually by the refractory behavior of EEG features commonly used in non-invasive BCI β paradigms, such as event-related desynchronizations (ERD) of pericentral µ rhythms or lateralized readiness potentials (LRP). Objective: We conducted a study in healthy subjects addressing (a) the stability of ERD and LRP for increasing rates of self-paced typewriting finger movements and (b) the possible ERD/LRP dependence on ipsi- vs. contralaterally preceding hand movements. Methods: 8 healthy subjects performed self-paced typewriting (randomized left vs. right index finger tapping on a computer keyboard) in blocks with 30, 60 or 120 keystrokes per minute. Using 64-channel EEG recordings, we compared -ERD depending on the preceding, intraindividually averaged LRP and µ movements. Results: Grand averages over 8 subjects showed for all parameters (LRP, -ERD) and tap rates activation profiles which differed significantly between µ ipsilaterally vs. contralaterally preceding finger movements: greater negativation (LRP) or, respectively, desynchronization (ERD), prior to EMG onset was found in case of contralaterally preceding taps compared to ipsilateral movements. Remarkably, the post-movement µ/β event-related synchronization (ERS) was stronger after repetitive ipsilateral movements. Conclusions: These findings demonstrate that motor-related EEG features depend on preceding finger movements in a typical BCI setting with self-paced finger movements. Importantly, these EEG features can be distinguished from each other so that classifiers could be trained for single-trial classifications in BCI applications.