Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.
Surgical neuromodulation through implantable devices allows for stimulation delivery to subcortical regions, crucial for symptom control in many debilitating neurological conditions. Novel closed-loop algorithms deliver therapy tailor-made to endogenous physiological activity, however rely on precise sensing of signals such as subcortical oscillations. The frequency of such intrinsic activity can vary depending on subcortical target nucleus, while factors such as regional anatomy may also contribute to variability in sensing signals. While artefact parameters have been explored in more 'standard' and commonly used targets (such as the basal ganglia, which are implanted in movement disorders), characterisation in novel candidate nuclei is still under investigation. One such important area is the brainstem, which contains nuclei crucial for arousal and autonomic regulation. The brainstem provides additional implantation targets for treatment indications in disorders of consciousness and sleep, yet poses distinct anatomical challenges compared to central subcortical targets. Here we investigate the region-specific artefacts encountered during activity and rest while streaming data from brainstem implants with a cranially-mounted device in two patients. Such artefacts result from this complex anatomical environment and its interactions with physiological parameters such as head movement and cardiac functions. The implications of the micromotion-induced artefacts, and potential mitigation, are then considered for future closed-loop stimulation methods.
Mental fatigue results in feelings of tiredness and decreases task performance and efficiency. It can be caused by cognitive underload (passive fatigue) or overload (active fatigue). Although fatigue detection models have been developed using electroencephalogram (EEG) recordings, it is unclear whether their predictions (derived from one task) are applicable to different tasks. We investigated if an EEG-based fatigue model previously trained on a passive fatigue-inducing driving task, can predict fatigue during an active fatigue-inducing task. We collected EEG while subjects performed a target-hitting task as a non-fatigued baseline, followed by mental arithmetic tasks of increasing complexity and mental load. Overall, the fatigue score from the passive fatigue model was significantly correlated with self-reported fatigue and sleepiness, which increased over the course of the experiment. The fatigue score was higher during simple mental arithmetic than the target-hitting task, demonstrating that the model can successfully differentiate non-fatigued and passive/low mental load situations. However, the fatigue score was higher during the simple (low mental load) than the complex (high mental load) mental arithmetic task, showing that the model cannot accurately detect active fatigue. Future fatigue models should be trained on a variety of both active and passive fatigue-inducing tasks to be more generalizable.
Virtual environments are often used in pre-wearing training and assessment of prosthetic control abilities. Here, we developed a virtual prosthetic hand training platform for evaluation of closed-loop control of grasp force. Biorealistic controllers emulated a pair of antagonistic muscles that actuated the thumb and index fingers of the hand. Surface electromyographic (sEMG) signals from a pair of antagonistic residual muscles drove the biorealistic controllers. Tactile forces from fingertip sensors were conveyed to amputees through evoked tactile sensations (ETS) elicited at the projected finger map (PFM) areas of the stump. A forearm amputee subject participated in force tracking or holding tasks using the virtual hand with residual muscle EMGs, or the contralateral intact hand. Root-mean-square error (RMSE) was used as outcome measure of motor performance. Results in this subject showed that the biorealistic controller enabled the virtual hand to track and maintain grasping forces. The best performance in both tasks was achieved by the contralateral intact hand with visual feedback. The roles of visual or tactile feedback in force tracking or maintaining were also assessed with the virtual hand. For force holding task, hybrid tactile and visual feedback with biorealistic control had a better performance than single visual or tactile feedback in terms of RMSE, success rate, and force variability. While in the force pursuing task, tactile feedback did not seem to add visual feedback in following the target force. The study suggests that training may be required for a novel virtual hand user to perceive and integrate multiple modalities of feedback information, so as to optimize the closed-loop control ability.
The classification of motor imagery in non-invasive brain-computer interface (BCI) is a challenge due to the high variation of brain evoked responses across users and the non-stationarity properties of the electroencephalography (EEG) signal. With different sessions from the same user, it is possible to find substantial differences that require the BCI system to be recalibrated. In clinical settings, it is therefore necessary to know when a system should be recalibrated or when the system should adapt itself to deal with the shifts in the signal, i.e., the covariate shift, and/or catch artefacts that deviate substantially from the original data distribution. In this paper, we propose to use density based one-class classifiers using distances based on the Riemannian geometry framework for assessing the distribution of the EEG signal in motor imagery BCI. We assess the performance of the algorithms with a database of 14 participants. The results show that sessions from the same person can be reliably detected using the proposed approach. We also assess how the one-class classifiers can be used to determine if it is necessary to run domain adaptation in the test phase. The results support the conclusion that the accuracy improves as the system is adapted to shifting domains in signals.
