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The differences in P300 latency, P300 amplitude, response selection, and reaction time between skilled and less-skilled cricket batsmen have been investigated. Eight skilled and ten less-skilled right-handed batsmen each viewed 100 in-swing, 100 out-swing, and 40 slower deliveries displayed in random sequence from projected video footage whilst their responses and electroencephalograms were recorded. Logistic regression was used to derive a discriminative function for the P300 data. This was done to determine whether the skilled batsmen differed from the less-skilled batsmen on the basis of pooled P300 amplitude and latency data. All the batsmen were correctly characterised as being skilled or less-skilled. Logistic regression equations with reaction time and correctness of response data indicated that behavioural data do not correctly classify skilled performance. It is suggested that skilled cricket batsmen have a superior perceptual decision-making ability compared with less-skilled cricket batsmen, as measured by P300 latency and amplitude. This appears to be the first study showing a link between skill and cerebral cortical activity during a perceptual cricket batting task and it could pave the way for future studies on mental processing in cricket batsmen.

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Brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.