Investigating the feasibility of combining EEG and EMG for controlling a hybrid human computer interface in patients with spinal cord injury

Abstract

Objective. Human-computer interfaces (HCI) are potential tools for assisting (movement replacement) and rehabilitating (movement restoration) individuals with spinal cord injury (SCI). HCIs based on electroencephalography (EEG) have limited accuracy and hence control options; this could be improved by exploiting potential residual muscle activity (electromyography, EMG). The study objectives were to determine if combined EEG and EMG improves offline singletrial movement classification. Furthermore, the effect of number of classes and detection latency on the accuracies was investigated. Methods. Ten able-bodied and eight SCI subjects performed elbow flexion/extension at three force levels while EEG and EMG were recorded. Temporal and spectral features were extracted from the EEG and Hudgins time domain features were extracted from the EMG in 1-second time windows. The time window was shifted (200-ms shift) over 5second epochs around the movement onset. Each segment was classified in three scenarios (2, 3 or 7 classes) using linear discriminant analysis. Results. The accuracies obtained with EEG (51.2%) was outperformed by EMG (95.5%) and combined EMG and EEG (96.2%). Immediately after the EMG onset, the accuracies increased and rapidly reached a plateau. High accuracies were obtained for the different number of classes. Conclusion and Significance. EMG was crucial for obtaining high accuracies, and potential residual EMG should be exploited in HCIs to improve the performance. Force proved to be a viable option for SCI subjects with residual EMG to increase the number of classes for HCI control. These findings could assist design considerations of HCIs for SCI individuals.

Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review

“…Test new rehabilitation paradigm [34,[58][59][60][61][62] Investigation of the efficacy of BMI [63] and EEG feedback [64] Study of movement classification combining EEG and EMG [65] Investigation of movement intention [66,67] and motor imagery detection [68] Study of the cortico-muscular coupling [69][70][71] Study of the neuroplasticity with electrical stimulation [72] Investigation of engagement in game rehabilitation [73] Study of the effects of the use of VR in facial rehabilitation [74] Randomized controlled trial Study the effects of transcranial [75,76] and peripheral electrical stimulation [77][78][79] Investigation of the efficacy of lower limb exoskeleton rehabilitation [80] Assessment of a novel gait training paradigm [81] Investigation of the efficacy of neurologic therapy based on music [82] Investigation of the effects of biofeedback [83] Methodological study Presentation of a multivariate approach for motor assessment [32,33] Presentation of a method for compressing EEG-EMG signal [84] Presentation of algorithms for motion detection [85,86] and motion classification [87,88] A total of 21 papers out of 55 (37%) presented observational studies in which functional parameters or effects of treatments were investigated on healthy subjects and patients. An aim commonly found in these works was the assessment of the cortico-muscular coupling during movements [37][38][39]…”

“…Donati et al [63] tested a multi-stage brain-machine interface (BMI), while Hashimoto et al [64] used the EEG feedback for improving rehabilitation. Some studies presented preliminary results for methods of movement classification [65], detection of movement intention [66,67], and motor imagery [68]. Three studies investigated cortico-muscular coupling [69][70][71] as a novel method to evaluate the motor recovery of post-stroke patients.…”

“…Parkinson disease: 40-80 [76]; Cerebral palsy: 5-19 [34]; Spinal cord injury: 32.5 ± 6.2 [38], 26-38 [63], 32 ± 6 [65], 43.5 ± 12.4 [53]; Writer's cramp: 67 [64]; Low back pain: 39.2 ± 6.33 [57]; Mixed injuries and diseases: 33-54 [73], 60-80 [59]; Facial palsy: 23 [74]; MCI: n.a. [85]; Cardiovascular diseases: 56.3 ± 1.0 [55] Nr of subjects ≤10 6 [37], 5 [32], 6 [34], 3 + 4 [58], 6 [61], 1 [84], 8 [63], 10 [72], 2 [62], 1 [64], 7 [66], 5 [47], 8 [67], 7 [52], 10 [33], 1 [74], 5 + 5 [40] Torso: [57]; Face: [74]; Neck: [68] 3.3.1.…”

“…Some studies assessed the motor function only with simple movements performed by subjects, such as wrist [69,84], elbow [38], arm [37,57,70,71,87,88], hand [40,67,83,86] and leg tasks [53] and respiratory movements [55]. Additional sensorial feedbacks were employed for evaluating the effects of visual [65,73], auditory [82], and audiovisual feedback [56]. In one paper, the additional neurofeedback allowed the improvement of functional recovery of the hand in dystonic patients [64].…”

“…Mirror visual feedback [50] Wrist movements [69,84]; wrist movements + visual feedback [73] Elbow movements [38]; elbow movements + visual feedback [65] Arm movements [37,57,70,71,87,88]; arm movements + auditory feedback [82]; arm movements + balance handle [52] Hand movements [40,67,83,86]; hand movements + motor imagery + auditory/smelling stimulus [85]; hand movements + audiovisual feedback [56]; hand movements + biofeedback [64] Leg movements [53] Motor imagery swallow and tongue protrusion [68] Respiratory movements [55] Oculus rift [74] Robotic assistance Exoskeleton lower limb + virtual reality [34]; exoskeleton lower limb + virtual reality + BMI [63]; exoskeleton lower limb + walking [47,48,80]; exoskeleton lower limb + treadmill [49] Robotic end effector upper limb [32,33,58,59] Robotic mirror therapy upper limb [60] Hand mobilizer exoskeleton [39,61,62]; hand mobilizer exoskeleton + motor imagery [66] Ankle mobilizer [41] Peripheral electrical stimulation FES + hand movements [43...…”