Spinal cord injury (SCI) is damage to any part of the spinal cord resulting in paralysis, bowel and/or bladder incontinence, and loss of sensation and other bodily functions. Current treatments for chronic SCI are focused on managing symptoms and preventing further damage to the spinal cord with limited neuro-restorative interventions. Recent research and independent clinical trials of spinal cord stimulation (SCS) or intensive neuro-rehabilitation including neuro-robotics in participants with SCI have suggested potential malleability of the neuronal networks for neurological recovery. We hypothesize that epidural electrical stimulation (EES) delivered via SCS in conjunction with mental imagery practice and robotic neuro-rehabilitation can synergistically improve volitional motor function below the level of injury in participants with chronic clinically motor-complete SCI. In our pilot clinical RESTORES trial (RESToration Of Rehabilitative function with Epidural spinal Stimulation), we investigate the feasibility of this combined multi-modal approach in restoring volitional motor control and achieving independent overground locomotion in participants with chronic motor complete thoracic SCI. Secondary aims are to assess the safety of this combination therapy including the off-label SCS usage as well as improving functional outcome measures. To our knowledge, this is the first clinical trial that investigates the combined impact of this multi-modal EES and rehabilitation strategy in participants with chronic motor complete SCI. Two participants with chronic motor-complete thoracic SCI were recruited for this pilot trial. Both participants have successfully regained volitional motor control below their level of SCI injury and achieved independent overground walking within a month of post-operative stimulation and rehabilitation. There were no adverse events noted in our trial and there was an improvement in post-operative truncal stability score. Results from this pilot study demonstrates the feasibility of combining EES, mental imagery practice and robotic rehabilitation in improving volitional motor control below level of SCI injury and restoring independent overground walking for participants with chronic motor-complete SCI. Our team believes that this provides very exciting promise in a field currently devoid of disease-modifying therapies.

Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain–Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI.

Multimodal brain–computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient’s specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant’s respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements.

Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer long short-term memory (LSTM)based artificial neural network (ANN) for decoding BMI neural signals. We collected motor cortical neural signals from a nonhuman primate (NHP), implanted with microelectrode array (MEA) while performing a directional joystick task. Next, we compared the LSTM model in decoding the joystick trajectories from the neural signals against the prevailing KF model. The results showed that the LSTM model yielded significantly improved decoding accuracy measured by mean correlation coefficient (0.84, p < 10 ^-7 ) than the KF model (0.72). In addition, using a principal component analysis (PCA)-based dimensionality reduction technique yielded slightly deteriorated accuracies for both the LSTM (0.80) and KF (0.70) models, but greatly reduced the computational complexity. The results showed that the LSTM decoding model holds promise to improve decoding in BMIs for paralyzed humans.

Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs.

Recruiting participants for clinical trials targeting specific populations, like patients with chronic motor complete spinal cord injuries (SCIs), is challenging. The RESTORES trial evaluated spinal cord stimulation (SCS) combined with robotic neurorehabilitation for motor recovery in this population. This feasibility study enrolled three participants to assess SCS implant safety, synergistic effects of SCS and robotic rehabilitation, and clinical outcomes. Key recruitment barriers included the small patient pool, stringent eligibility criteria, patient skepticism, and logistical and emotional challenges. Strategies to address these challenges encompassed multidisciplinary collaborations with clinical centers, SCI associations, and patient support groups, including pre-surgical counselling and transparent communication. A dedicated clinical research coordinator ensured ethical compliance, logistical support, and participant engagement. Travel reimbursements, family involvement, and peer advocacy fostered accessibility and trust. Of the 115 patients screened, only 3 met the strict eligibility criteria, due to high screening failure rates and participant apprehension. Peer testimonials and family support helped enhance motivation and adherence. Ethical safeguards, like a data safety monitoring board, ensured participant safety and transparency. The RESTORES trial underscores the complexity of recruiting for pioneering interventions while highlighting the importance of tailored, patient-centric strategies. Insights gained will inform future trials and contribute to advancing SCI rehabilitation, offering hope for enhanced neurological recovery and quality of life for individuals with chronic motor complete SCI.