Classification based on sparse representations of attributes derived from empirical mode decomposition in a multiclass problem of motor imagery in EEG signals

ConclusionThe improvement of self-adaptive mechanisms that respond efficiently to the user ’s context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representat ion in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.
Source: Health and Technology - Category: Information Technology Source Type: research