Combining discriminant and topographic information in BCI: Preliminary results on stroke patients

Abstract

Abstract-Non-Invasive Brain-Computer Interfaces (BCI) convey a great potential in the field of stroke rehabilitation, where the continuous monitoring of mental tasks execution could support the positive effects of standard therapies. In this paper we combine time-frequency analysis of EEG with the topographic analysis to identify and track task-related patterns of brain activity emerging during a single BCI session. 6 Stroke patients executed Motor Imagery of the affected and unaffected hands: EEG sites were ranked depending on their discriminant power (DP) at different time instants and the resulting discriminant periods were used as a prior to extract EEG Microstates. Results show that the combination of these two techniques can provide insights about specific motor-related processes happening at a fine grain temporal resolution. Such events, represented by EEG microstates, can be tracked and used both to quantify changes of underlying neural structures and to provide feedback to patients and therapists.

Brain-Computer Interface in Stroke Rehabilitation

“…Subsequently, there was an increase in the number of papers that reported methodology studies on stroke patients [21][22][23][24][25][26][27][28][29][30][31], and henceforth we will only briefly highlight some of these studies that we found exhibited distinctiveness from other studies. Besides targeting the hand and the upper limb functions, Niazi et al [23] reported a study on 5 stroke patients targeting ME of the lower limb ankle function.…”

Brain-Computer Interface for Stroke Rehabilitation

“…Subsequently, there was an increase in the number of papers that reported methodology studies on stroke patients [21][22][23][24][25][26][27][28][29][30][31], and henceforth we will only briefly highlight some of these studies that we found exhibited distinctiveness from other studies. Besides targeting the hand and the upper limb functions, Niazi et al [23] reported a study on 5 stroke patients targeting ME of the lower limb ankle function.…”

Brain-Machine Interfaces

“…Apart from these 'classical' imagined movements of hands, arms and feet, recently researchers have also shown the feasibility of recognizing different wrist movements [46,47]. Also, as mentioned before, recent results show how it is possible to decode 3D arm trajectories from EEG [42,45] as well as basic grasping patterns such as hand closing/opening [15,48] and finger extension [49,50]. Although more work remains to be done in this area, its combination with functional electrical stimulation and light exoskeletons (see Fig.…”