Journal of Neuroscience Methods | 2021
A new neurofeedback training method based on feature space clustering to control EEG features within target clusters
Abstract
BACKGROUND\nWithin the most commonly used neurofeedback training methods, a threshold has been defined for each training feature wherein subjects status during training can be assessed according to the given value. In the present study, a neurofeedback training method based on feature-space clustering was proposed in order to assess subjects status more accurately.\n\n\nNEW METHOD\nNeural gas algorithm was employed for feature space clustering. Then, the clusters were labeled as initial clusters (where the EEG features were placed prior to training) and target (where the EEG features should be shifted towards during training) ones. A scoring index was defined whose value was determined according to subjects brain activity. This method was simulated in two versions: soft-boundary and hard-boundary based methods.\n\n\nRESULTS\nThe results of the present simulation showed that the proposed hard-boundary based version could guide the subjects towards the boundaries of the target clusters and even their status would be stabilized in case of too many changes in subjects training features. In the proposed soft-boundary based version, in case of too many changes in training features, the subjects would not be encouraged and they could be guided towards the target boundaries.\n\n\nCONCLUSION\nThe proposed hard-boundary based version could be effective in guiding a subject towards being placed within the boundaries of target clusters and even beyond them if no specific limits exited for training features. As well, the soft-boundary based version could be useful when controlling training features within a limit.