Classification of EEG signals to identify variations in attention during motor task execution

Background: Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control
external devices. User status such as attention affects BCI performance;thus detecting the user’s attention
drift due to internal or external factors is essential for high detection accuracy.
New method: An auditory oddball task was applied to divert the users’ attention during a simple ankle
dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal
and time-frequency features were projected to a lower dimension space and used to analyze the effect of
two attentionlevels onmotor tasks ineachparticipant. Then, a globalfeature distribution was constructed
with the projected time-frequency features of all participants from all channels and applied for attention
classification during motor movement execution.
Results: Time-frequency features led to significantly better classification results with respect to the temporal
features, particularly for electrodes located over the motor cortex. Motor cortex channels had a
higher accuracy in comparison to other channels in the global discrimination of attention level.
Comparing with existing methods: Previous methods have used the attention to a task to drive external
devices, such as the P300 speller. However, here we focus for the first time on the effect of attention drift
while performing a motor task.
Conclusions: It is possible to explore user’s attention variation when performing motor tasks in synchronous
BCI systems with time-frequency features. This is the first step towards an adaptive real-time
BCI with an integrated function to reveal attention shifts from the motor task.