Movement Type Prediction before Its Onset Using Signals from Prefrontal Area: An Electrocorticography Study

Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (-2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13-30Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.open

Dynamic Frequency and Multi-site Cortical Stimulation for Inducing Artificial Somatosensation: A Preliminary Study

Eliciting artificial somatosensation using brain stimulation has become one of the most essential techniques in closed-loop brain machine interface (BMI) system to control robotic limb. To date, however, clinical researchers have struggled to precisely control the quality of induced artificial somatosensation due to limited stimulation methods. Here, we developed dynamic frequency and multi-site cortical stimulation methods using clinically approved cortical stimulators. Additionally, we report results of our tests on one patient to validate these techniques.N

Interference in tactile discrmination performance by neuronal modulation

Perceiving and processing sensory stimuli are essential to generate motor action. Previous studies suggested features of vibrotactile stimulus such as amplitude and frequency are differently represented onto somatosensory cortices so that the stimulus can be discriminated. In the present study, we aimed to demonstrate the effect of transcranial magnetic stimulation (TMS) triplet pulses over primary somatosensory cortex (S1) or secondary somatosensory cortex (S2) on a tactile discrimination task. In two alternative forced choice task, TMS over Si or S2 significantly interfered with the discrimination performance. This disruptive influence was mostly shown when the vibrotactile stimulus was close to high frequency (320Hz). Therefore we concluded the inhibitory effect of TMS is selective with tactile frequency.N

Macroscopic Aspects of Bi-directional BCI in Human

Brain computer interface (BCI) techniques have been remarkably developed in the last decades. Recently, researchers have investigated the sensorimotor integrated bi-directional BCI using micro-recordings. However, this approach has suffered from the insufficient coverage of cortical surface leading to the limited applicability. In this study, we focus on several macroscopic aspects to overcome the limitation by direct cortical stimulation (DCS) and ECoG-based movement decoding. We found that cortical areas elicited somatosensation by DCS are wide spread beyond the S1. In addition, we showed that movement imagery is successfully decoded by multi-regional neural population activities beyond the primary areas.N

Movement-Related Sensorimotor High-Gamma Activity Mainly Represents Somatosensory Feedback

Somatosensation plays pivotal roles in the everyday motor control of humans. During active movement, there exists a prominent high-gamma (HG >50 Hz) power increase in the primary somatosensory cortex (S1), and this provides an important feature in relation to the decoding of movement in a brain-machine interface (BMI). However, one concern of BMI researchers is the inflation of the decoding performance due to the activation of somatosensory feedback, which is not elicited in patients who have lost their sensorimotor function. In fact, it is unclear as to how much the HG component activated in S1 contributes to the overall sensorimotor HG power during voluntary movement. With regard to other functional roles of HG in S1, recent findings have reported that these HG power levels increase before the onset of actual movement, which implies neural activation for top-down movement preparation or sensorimotor interaction, i.e., an efference copy. These results are promising for BMI applications but remain inconclusive. Here, we found using electrocorticography (ECoG) from eight patients that HG activation in S1 is stronger and more informative than it is in the primary motor cortex (M1) regardless of the type of movement. We also demonstrate by means of electromyography (EMG) that the onset timing of the HG power in S1 is later (49 ms) than that of the actual movement. Interestingly, we show that the HG power fluctuations in S1 are closely related to subtle muscle contractions, even during the pre-movement period. These results suggest the following: (1) movement-related HG activity in S1 strongly affects the overall sensorimotor HG power, and (2) HG activity in S1 during voluntary movement mainly represents cortical neural processing for somatosensory feedback

Decoding trajectories of imagined hand movement using electrocorticograms for brain-machine interface

Objective. Reaching hand movement is an important motor skill actively examined in the brain-computer interface (BCI). Among the various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement. Approach. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action. Main results. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson's correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively. Significance. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.N

Intra- and inter-regional connectivity evolution in time (Subject 3).

<p>The strongly connected pairs are presented as red lines on a cortical surface model at every 200ms. It clearly shows different temporal connectivity dynamics over the movement time.</p

Temporal connectivity dynamics in DLPFC, 1M, and DLPFC-1M networks.

<p>Temporal connectivity dynamics are represented by the onset and offset of the movement, respectively. Pink vertical dotted line denotes movement onset (A) and offset (B).</p