Evaluating the effects of multimodal EEG-fNIRS neurofeedback for motor imagery: An experimental platform and study...

Neurofeedback (NF) enables the self-regulation of brain activity through real-time feedback extracted from brain measures. Recently, the combination of several neuroimaging methods to characterize brain activity has led to growing interest in NF. The integration of various portable recording techniques, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), respectively based on electrical and hemodynamic activity, could enhance the characterization of brain responses and subsequently improve NF performance. Such multimodal NF used with motor imagery (MI) could benefit post-stroke motor rehabilitation to stimulate neuroplasticity of the lesioned motor areas. Nevertheless, their concomitant use in NF to identify brain activity features during upper-limb MI-based NF has not been studied to our knowledge. The objective of this paper is to present our fully operational experimental platform and the study protocol we propose to assess the benefits of combining EEG and fNIRS for NF in the context of MI.

We developed a custom experimental platform, including a cap integrating EEG and fNIRS sensors, along with software for real-time signal processing, NF score calculation and visual feedback presentation. To evaluate the effect of EEG-fNIRS NF in the context of MI, thirty right-handed participants will undergo three randomized NF conditions: EEG-only, fNIRS-only and EEG-fNIRS based NF. EEG electrodes and fNIRS channels will be positioned above the sensorimotor cortices. Participants will be presented with a visual representation of a ball along a one-dimensional gauge moving upwards according to their brain activity level as they perform a MI task of their left-hand. An NF score will be computed from the right primary motor cortex activity along the three different experimental conditions (EEG-only, fNIRS-only, EEG-fNIRS). The association between the NF score, the neuroimaging modality and the motor imagery strategy will then be analyzed.

The designed experimental platform allows combined EEG-fNIRS NF. The presented study protocol will be the first investigation of the effects of multimodal neurofeedback using EEG and fNIRS during upper-limb MI tasks. We hypothesize that presenting the participants with a visual NF representing a score based on both EEG and fNIRS signals will result in more specific task-related brain activity in the sensorimotor cortices. With potentially increased neuroplasticity, such a system could find applications in clinical contexts, particularly in motor rehabilitation, for example in post-stroke units.