EEG Neurofeedback Training (NFT) is a non-invasive neurophysiological technique, aimed at facilitating learned self-regulation of electrical activity of the brain. Beyond research into clinical applications of neurofeedback, a relative surge of interest into the methodology has led to attempts to apply EEG neurofeedback as a means to facilitate performance enhancement among non-clinical populations. One such domain is the enhancement of athletic performance and motor function. While significant attempts have been undertaken to investigate outcomes of sensori-motor rhythm (12-15Hz) modulation on aspects of motor performance, research exploring behavioural outcomes of EEG-NFT targeting the beta frequencies (15-20Hz) appear scarce.
Based upon the current platform of research, measures of beta, and beta activity of the primary motor cortex have been evidenced to relate to aspects of motor skill acquisition and learning – and likely as a process underlying motor inhibition. Support for such claims may be seen in research highlighting excessive beta frequency activity in individuals with disorders of motor function, and increased event-related beta desynchronization among elite vs amateur athletes. As such, the current research project attempts to investigate behavioural outcomes directly following beta EEG-NFT, as measured by a dart-throwing accuracy task and a visual Go/NoGo task measuring inhibitory processes.
Consisting of 17 participants, the project employs a double-blind design with each participant undertaking two 30-minute sessions of neurofeedback across two testing conditions. Both participant and facilitator are blind to the feedback condition. It is hypothesised that within the beta+ (increase) condition, participants will evidence an improvement in dart-throw accuracy, as compared to results from the beta-(decrease) condition. It is hypothesised that within the beta+ condition, participants will evidence reduced errors of commission and omission, alongside increases of reaction time within a modified Go/NoGo performance task as compared to the beta- condition. Supervisor: Dr David White.