Mads Jochumsen

Detection and classification of movement-related cortical potentials associated with task force and speed

OBJECTIVE In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. APPROACH Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. MAIN RESULTS The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset. SIGNIFICANCE The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.

Comparison of spatial filters and features for the detection and classification of movement-related cortical potentials in healthy individuals and stroke patients

OBJECTIVE The possibility of detecting movement-related cortical potentials (MRCPs) at the single trial level has been explored for closing the motor control loop with brain-computer interfaces (BCIs) for neurorehabilitation. A distinct feature of MRCPs is that the movement kinetic information is encoded in the brain potential prior to the onset of the movement, which makes it possible to timely drive external devices to provide sensory feedback according to the efferent activity from the brain. The aim of this study was to compare methods for the detection (different spatial filters) and classification (features extracted from various domains) of MRCPs from continuous electroencephalography recordings from executed and imagined movements from healthy subjects (n = 24) and attempted movements from stroke patients (n = 6) to optimize the performance of MRCP-based BCIs for neurorehabilitation. APPROACH The MRCPs from four cue-based tasks were detected with a template matching approach and a set of spatial filters, and classified with a linear support vector machine using the combination of temporal, spectral, time-scale, or entropy-based features. MAIN RESULTS The best spatial filter (large Laplacian spatial filter (LLSF)) resulted in a true positive rate of 82 ± 9%, 78 ± 12% and 72 ± 9% (with detections occurring ∼ 200 ms before the onset of the movement) for executed, imagined and attempted movements (stroke patients). The best feature combination (temporal and spectral) led to pairwise classification of 73 ± 9%, 64 ± 10% and 80 ± 12%. When the detection was combined with classification, 60 ± 10%, 49 ± 10% and 58 ± 10% of the movements were both correctly detected and classified for executed, imagined and attempted movements. A similar performance for detection and classification was obtained with optimized spatial filtering. SIGNIFICANCE A simple setup with an LLSF is useful for detecting cued movements while the combination of features from the time and frequency domain can optimize the decoding of kinetic information from MRCPs; this may be used in neuromodulatory BCIs.

Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG

OBJECTIVE To detect movement intention from executed and imaginary palmar grasps in healthy subjects and attempted executions in stroke patients using one EEG channel. Moreover, movement force and speed were also decoded. APPROACH Fifteen healthy subjects performed motor execution and imagination of four types of palmar grasps. In addition, five stroke patients attempted to perform the same movements. The movements were detected from the continuous EEG using a single electrode/channel overlying the cortical representation of the hand. Four features were extracted from the EEG signal and classified with a support vector machine (SVM) to decode the level of force and speed associated with the movement. The system performance was evaluated based on both detection and classification. MAIN RESULTS ∼ 75% of all movements (executed, imaginary and attempted) were detected 100 ms before the onset of the movement. ∼ 60% of the movements were correctly classified according to the intended level of force and speed. When detection and classification were combined, ∼ 45% of the movements were correctly detected and classified in both the healthy and stroke subjects, although the performance was slightly better in healthy subjects. SIGNIFICANCE The results indicate that it is possible to use a single EEG channel for detecting movement intentions that may be combined with assistive technologies. The simple setup may lead to a smoother transition from laboratory tests to the clinic.

Manipulation of dysfunctional spinal joints affects sensorimotor integration in the prefrontal cortex: a brain source localization study

Objectives. Studies have shown decreases in N30 somatosensory evoked potential (SEP) peak amplitudes following spinal manipulation (SM) of dysfunctional segments in subclinical pain (SCP) populations. This study sought to verify these findings and to investigate underlying brain sources that may be responsible for such changes. Methods. Nineteen SCP volunteers attended two experimental sessions, SM and control in random order. SEPs from 62-channel EEG cap were recorded following median nerve stimulation (1000 stimuli at 2.3 Hz) before and after either intervention. Peak-to-peak amplitude and latency analysis was completed for different SEPs peak. Dipolar models of underlying brain sources were built by using the brain electrical source analysis. Two-way repeated measures ANOVA was used to assessed differences in N30 amplitudes, dipole locations, and dipole strengths. Results. SM decreased the N30 amplitude by 16.9 ± 31.3% (P = 0.02), while no differences were seen following the control intervention (P = 0.4). Brain source modeling revealed a 4-source model but only the prefrontal source showed reduced activity by 20.2 ± 12.2% (P = 0.03) following SM. Conclusion. A single session of spinal manipulation of dysfunctional segments in subclinical pain patients alters somatosensory processing at the cortical level, particularly within the prefrontal cortex.

Comparison of features for movement prediction from single-trial movement-related cortical potentials in healthy subjects and stroke patients

Detection of movement intention from the movement-related cortical potential (MRCP) derived from the electroencephalogram (EEG) signals has shown to be important in combination with assistive devices for effective neurofeedback in rehabilitation. In this study, we compare time and frequency domain features to detect movement intention from EEG signals prior to movement execution. Data were recoded from 24 able-bodied subjects, 12 performing real movements, and 12 performing imaginary movements. Furthermore, six stroke patients with lower limb paresis were included. Temporal and spectral features were investigated in combination with linear discriminant analysis and compared with template matching. The results showed that spectral features were best suited for differentiating between movement intention and noise across different tasks. The ensemble average across tasks when using spectral features was (error = 3.4 ± 0.8%, sensitivity = 97.2 ± 0.9%, and specificity = 97 ± 1%) significantly better (P < 0.01) than temporal features (error = 15 ± 1.4%, sensitivity: 85 ± 1.3%, and specificity: 84 ± 2%). The proposed approach also (error = 3.4 ± 0.8%) outperformed template matching (error = 26.9 ± 2.3%) significantly (P > 0.001). Results imply that frequency information is important for detecting movement intention, which is promising for the application of this approach to provide patient-driven real-time neurofeedback.

Detection of Movement Intentions through a Single Channel of Electroencephalography

In this study, the aim was to estimate the performance of a brain-computer interface (BCI) system by detecting movement intentions using only a single monopolar channel of electroencephalography (EEG). Seven healthy subjects performed four types of cued palmar grasps with two levels of force and speed. The movement intentions were detected using a technique where a template of the initial negative phase of the movement-related cortical potential (movement intention) was matched with the continuous EEG. On average 78 % of the movements were detected (true positive rate) ~150 milliseconds before the task onset. The number of false positive detections was 1.5 per minute. The estimated system performance, using only a single monopolar channel, indicates that the proposed setup can be used for neuromodulation paradigms in BCI where the movement intention is paired with somatosensory feedback from e.g. functional electrical stimulation or robot-assisted movements.

A review of techniques for detection of movement intention using movement-related cortical potentials

The movement-related cortical potential (MRCP) is a low-frequency negative shift in the electroencephalography (EEG) recording that takes place about 2 seconds prior to voluntary movement production. MRCP replicates the cortical processes employed in planning and preparation of movement. In this study, we recapitulate the features such as signals acquisition, processing, and enhancement and different electrode montages used for EEG data recoding from different studies that used MRCPs to predict the upcoming real or imaginary movement. An authentic identification of human movement intention, accompanying the knowledge of the limb engaged in the performance and its direction of movement, has a potential implication in the control of external devices. This information could be helpful in development of a proficient patient-driven rehabilitation tool based on brain-computer interfaces (BCIs). Such a BCI paradigm with shorter response time appears more natural to the amputees and can also induce plasticity in brain. Along with different training schedules, this can lead to restoration of motor control in stroke patients.

Impact of spinal manipulation on cortical drive to upper and lower limb muscles

This study investigates whether spinal manipulation leads to changes in motor control by measuring the recruitment pattern of motor units in both an upper and lower limb muscle and to see whether such changes may at least in part occur at the cortical level by recording movement related cortical potential (MRCP) amplitudes. In experiment one, transcranial magnetic stimulation input–output (TMS I/O) curves for an upper limb muscle (abductor pollicus brevis; APB) were recorded, along with F waves before and after either spinal manipulation or a control intervention for the same subjects on two different days. During two separate days, lower limb TMS I/O curves and MRCPs were recorded from tibialis anterior muscle (TA) pre and post spinal manipulation. Dependent measures were compared with repeated measures analysis of variance, with p set at 0.05. Spinal manipulation resulted in a 54.5% ± 93.1% increase in maximum motor evoked potential (MEPmax) for APB and a 44.6% ± 69.6% increase in MEPmax for TA. For the MRCP data following spinal manipulation there were significant difference for amplitude of early bereitschafts-potential (EBP), late bereitschafts potential (LBP) and also for peak negativity (PN). The results of this study show that spinal manipulation leads to changes in cortical excitability, as measured by significantly larger MEPmax for TMS induced input–output curves for both an upper and lower limb muscle, and with larger amplitudes of MRCP component post manipulation. No changes in spinal measures (i.e., F wave amplitudes or persistence) were observed, and no changes were shown following the control condition. These results are consistent with previous findings that have suggested increases in strength following spinal manipulation were due to descending cortical drive and could not be explained by changes at the level of the spinal cord. Spinal manipulation may therefore be indicated for the patients who have lost tonus of their muscle and/or are recovering from muscle degrading dysfunctions such as stroke or orthopaedic operations and/or may also be of interest to sports performers. These findings should be followed up in the relevant populations.

Induction of Long-term Depression-like Plasticity by Pairings of Motor Imagination and Peripheral Electrical Stimulation

Long-term depression (LTD) and long-term potentiation (LTP)-like plasticity are models of synaptic plasticity which have been associated with memory and learning. The induction of LTD and LTP-like plasticity, using different stimulation protocols, has been proposed as a means of addressing abnormalities in cortical excitability associated with conditions such as focal hand dystonia and stroke. The aim of this study was to investigate whether the excitability of the cortical projections to the tibialis anterior (TA) muscle could be decreased when dorsiflexion of the ankle joint was imagined and paired with peripheral electrical stimulation (ES) of the nerve supplying the antagonist soleus muscle. The effect of stimulus timing was evaluated by comparing paired stimulation timed to reach the cortex before, at and after the onset of imagined movement. Fourteen healthy subjects participated in six experimental sessions held on non-consecutive days. The timing of stimulation delivery was determined offline based on the contingent negative variation (CNV) of electroencephalography brain data obtained during imagined dorsiflexion. Afferent stimulation was provided via a single pulse ES to the peripheral nerve paired, based on the CNV, with motor imagination of ankle dorsiflexion. A significant decrease (P = 0.001) in the excitability of the cortical projection of TA was observed when the afferent volley from the ES of the tibial nerve (TN) reached the cortex at the onset of motor imagination based on the CNV. When TN stimulation was delivered before (P = 0.62), or after (P = 0.23) imagined movement onset there was no significant effect. Nor was a significant effect found when ES of the TN was applied independent of imagined movement (P = 0.45). Therefore, the excitability of the cortical projection to a muscle can be inhibited when ES of the nerve supplying the antagonist muscle is precisely paired with the onset of imagined movement.

Classifying speed and force from movement intentions using entropy and a support vector machine

In this work, we classified movement-related cortical potentials (MRCPs) associated with two levels of task force and speed with a linear and an optimized support vector machine (SVM). Features were extracted using Approximate Entropy (ApEn), Sample Entropy (SaEn) and Permutation Entropy (PeEn) calculated from the initial negative phase of the MRCP. Classification accuracies for the optimized SVM reached 68 ± 7% and 71 ± 10% (force and speed, respectively); with the linear SVM they reached 59 ± 8% and 64 ± 13%.