Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals

A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI

In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.

Improvement of grasping after motor imagery in C6-C7 tetraplegia: A kinematic and MEG pilot study

PURPOSE

Grasp recovery after C6-C7-spinal cord injury (SCI) requires learning "tenodesis grasp" whereby active wrist extension elicits passive thumb-to-forefinger and finger-to-palm flexion. Evidence that motor imagery (MI) promotes upper limb function after tetraplegia is growing, but whether MI potentiates grasp recovery in C6-C7-SCI individuals who have successfully learned the "tenodesis grasp" remains unknown.

METHODS

Six chronic stable C6-C7-SCI inpatients and six healthy control participants were included. C6-C7-SCI participants imagined grasping movements and controls visualized geometric forms for 45 minutes, three times a week for five weeks. Three separate measures taken over a five week period before the intervention formed the baseline. Intervention effects were assessed immediately after the intervention and eight weeks later. Each testing session consisted of kinematic recordings during reach-to-grasp and magnetoencephalographic (MEG) recordings during wrist extension.

RESULTS

During baseline, kinematic wrist extension angle during "tenodesis grasp" and MEG contralateral sensorimotor cortex (cSMC) activity during wrist extension were stable. Moreover, SCI participants exhibited a greater number of voxels within cSMC than controls. After MI sessions, wrist extension angle increased during "tenodesis grasp" and the number of voxels within cSMC during wrist extension decreased and became similar to controls.

CONCLUSION

These findings provide further support for the use of MI to reinforce a compensatory grasping movement (tenodesis) and induce brain plasticity.

Motor inhibition during motor imagery: a MEG study with a quadriplegic patient

The neurophysiological substrates underlying motor imagery are now well established. However, the neural processes of motor inhibition while mentally rehearsing an action are poorly understood. This concern has received limited experimental investigations leading to divergent conclusions. Whether motor command suppression is mediated by specific brain structures or by intracortical facilitation/inhibition is a matter of debate. Interestingly, although motor commands are inhibited during motor imagery (MI) in healthy participants, spinal cord injury may result in weakened motor inhibition. Using magentoencephalography, we observed that mental and actual execution of a goal-directed pointing task elicited similar primary motor cortex activation in a C6-C7 quadriplegic patient, thus confirming the hypothesis of weakened motor inhibition during MI. In an age-matched healthy control participant, however, primary motor area activation during MI was significantly reduced compared to physical practice. Brain activation during actual movement resulted in enhanced recruitment of premotor areas in the patient. In the healthy participant, we found functional relationships between the primary motor area and peri-rolandic sites including the primary sensory area and the supplementary motor area during MI. This neural network was not activated when the quadriplegic patient performed MI. We assume that the primary sensory area and the supplementary motor area may be part of a functional network underlying motor inhibition during MI. These data provide insights into brain function changes due to neuroplasticity after spinal cord injury and evidence cortical substrates underlying weakened motor inhibition during MI after deafferentation and deefferentation.

Gamma band activity associated with BCI performance: simultaneous MEG/EEG study

While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called “BCI-Illiterates”) cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.

Semantic classical conditioning and brain-computer interface control: encoding of affirmative and negative thinking

The aim of the study was to investigate conditioned electroencephalography (EEG) responses to factually correct and incorrect statements in order to enable binary communication by means of a brain-computer interface (BCI). In two experiments with healthy participants true and false statements (serving as conditioned stimuli, CSs) were paired with two different tones which served as unconditioned stimuli (USs). The features of the USs were varied and tested for their effectiveness to elicit differentiable conditioned reactions (CRs). After acquisition of the CRs, these CRs to true and false statements were classified offline using a radial basis function kernel support vector machine. A mean single-trial classification accuracy of 50.5% was achieved for differentiating conditioned “yes” versus “no” thinking and mean accuracies of 65.4% for classification of “yes” and 68.8% for “no” thinking (both relative to baseline) were found using the best US. Analysis of the area under the curve of the conditioned EEG responses revealed significant differences between conditioned “yes” and “no” answers. Even though improvements are necessary, these first results indicate that the semantic conditioning paradigm could be a useful basis for further research regarding BCI communication in patients in the complete locked-in state.

Neural decoding of unilateral upper limb movements using single trial MEG signals

A brain machine interface (BMI) provides the possibility of controlling such external devices as prosthetic arms for patients with severe motor dysfunction using their own brain signals. However, there have been few studies investigating the decoding accuracy for multiclasses of useful unilateral upper limb movements using non-invasive measurements. We investigated the decoding accuracy for classifying three types of unilateral upper limb movements using single-trial magnetoencephalography (MEG) signals. Neuromagnetic activities were recorded in 9 healthy subjects performing 3 types of right upper limb movements: hand grasping, pinching, and elbow flexion. A support vector machine was used to classify the single-trial MEG signals. The movement types were predicted with an average accuracy of 66 ± 10% (chance level: 33.3%) using neuromagnetic activity during a 400-ms interval (-200 ms to 200 ms from movement onsets). To explore the time-dependency of the decoding accuracy, we also examined the time course of decoding accuracy in 50-ms sliding windows from -500 ms to 500 ms. Decoding accuracies significantly increased and peaked once before (50.1 ± 4.9%) and twice after (58.5 ± 7.5% and 64.4 ± 7.6%) movement onsets in all subjects. Significant variability in the decoding features in the first peak was evident in the channels over the parietal area and in the second and third peaks in the channels over the sensorimotor area. Our results indicate that the three types of unilateral upper limb movement can be inferred with high accuracy by detecting differences in movement-related brain activity in the parietal and sensorimotor areas.

Prediction of human voluntary movement before it occurs

OBJECTIVE

Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5 s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME.

METHODS

We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace.

RESULTS

The average online prediction time was 0.62±0.25 s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement.

CONCLUSION

Human voluntary movement can be predicted before movement occurs.

SIGNIFICANCE

The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control.