Movement Classification using ECoG High-Gamma Powers from Human Sensorimotor Area during Active Movement Seokyun Ryun Interdisciplinary Program in Neuroscience Seoul National University Seoul, Korea
[email protected]
Abstract-Neural activation in high-gamma range robustly
observed
neurophysiological dominant active
in
studies
sensorimotor
movement.
sensorimotor
Here,
have
we
that
power
demonstrate
(>50
area.
indicated
high-gamma
June Sie Kim, Eunjeong Jeon and Chun Kee Chung Department ofBrain & Cognitive Sciences Seoul National University Seoul, Korea
[email protected] [email protected] [email protected]
there
changes
that
Hz) is
two
during
different
movement types (hand grasping and elbow tlection) can be the spatial dynamics of high-gamma features from primary motor cortex. Based on our results, we propose that sensorimotor high-gamma activities during active movement can be a powerful for
on-going
movement
classification,
and
their
characteristics mainly represent the instant movement states.
Keywords-component; Sensorimotor cortex, interface (BMI), high-gamma, active movement
I.
METHODS
are
discriminated at single-trial conditions with high accuracy using
feature
11.
Previous
brain-machine
INTRODUCTION
High-gamma (>50 Hz) neuronal population activities have been extensively observed in various cortical regions including prefrontal [1], visual [2], auditory [3], and sensorimotor areas [4]. Although their exact mechanism and ftmction remain to be elucidated, previous studies have suggested that these activities represent the increase of neuronal fuing rates [5] and on-going information coding [6]. In sensorimotor area, previous studies have indicated that dominant high-gamma power changes were observed during movement [7]. Also, their spatial distributions exhibit robust somatotopic organizations depending on the specific movement tasks. Given these electrophysiological evidence, the spatial patterns of high-gamma powers would be an appropriate feature for BMI application. Furthermore, because the movement-evoked sensorimotor high-gamma powers frequently show instant responses, the features from these powers are well-suited for real-time BMI system. In this study, we investigate whether the spatial patterns of sensorimotor high-gamma powers are efficient for single-trial movement type classification.
A.
Subjects
Electrocorticography (ECoG) data from four patients (2 fernales, aged 25-36 yr) with intractable epilepsy were used in this study. Subdural ECoG electrodes (Ad-tech Medical Instrument, Racine, WI, USA) had 4 mm diameter and 10 mm inter-electrode distance. Electrodes covered various brain areas including SI and MI. Pre-operative magnetic resonance imaging (MRI) data and post-operative computed tomography (CT) data were recorded from all subjects. All study procedures were approved by the Institutional Review Board of Seoul National University Hospital (H-0912-067-304), and all subjects received written informed consent before experiment. B.
Experimental Design and Data Acquisition
All subjects performed self-paced hand grasping and elbow flection by their hand or elbow contralateral to the implantation site. The subjects were instructed to have the resting period of approximately 5 s between each tasks, but not to count the number of seconds during this periods. They performed the tasks repetitively during 5 minutes and had 2 minutes of rest between sessions. We acquired three sessions from each movement except subject 4 (2 sessions each; due to the limited experimental time). ECoG data were recorded by using 128-channel amplifier system (Telefactor Beehive Horizon with an AURA LTM, Natus Neurology, West Warwick, RI, USA; Neuroscan, Charlotte, NC, USA). The reference site was cheekbone of each subject. Signals were digitized at 200 (Subject 1), 400 (Subject 2 and 3) and 1000 Hz (Subject 4), and band-pass filtered at 0.1-80 Hz, 0.1-150 Hz and 0.1-200 Hz, respectively. ECoG electrodes which show abnormal signal patterns due to the technical problems and epileptiform activities were excluded from further analysis. To detect the onset and offset of each movement task, we recorded electromyography (EMG) from the opponens pollicis for hand grasping, and from the biceps brachii for elbow flection.
C.
Data Analysis
Preprocessing All analysis were perfonned using MATLAB (Mathworks, Natick, MA, USA) and CURRY (version 7.0, Compumedics Neuroscan, Charlotte, NC, USA). The recorded data were re-referenced to the common average reference (CAR). To remove the noise from electrical devices, data were 60 Hz notch filtered with a fmite impulse response (FIR) filter using eegfilt in EEGLAB toolbox. To detennine onset/offset of movements, EMG signals were high-pass filtered at 2 Hz. We then applied Hilbert transform and took absolute value for calculating envelope signal. These data were nonnalized by the resting period data, and the onsetloffset points were semi-automatically detected by custom-made detection algorithm based on the threshold, duration and local minima. To localize ECoG electrodes, MRI-CT co-registration was automatically performed using CURRY, and the electrodes on the cortex were determined by visual inspection. Finally, we constructed 3-D cortex model and depicted electrode locations on this model. Data Analysis To remove very low frequency components, ECoG data were FIR high-pass filtered at I Hz. For Subject 4, 200 Hz low-pass filtering and 400 Hz down-sampling were perfonned to decrease computational load. Epoching was performed with a window of -2 s of movement onset to I s of movement offset for each sessions and movement types. To obtain time-frequency data, we used continuous Morlet Wavelet transfonn with frequency range of 5 to 100 Hz (80 Hz for Subject I), and calculated power time series of each frequencies. The transfonned data were nonnalized by the each frequency power of the resting periods (- 0.75 s to -0.25 s). To remove abnonnal transient peaks due to artifacts, the normalized data were median-fiItered with 1110 of sampling frequency. High-gamma band (50-80 Hz for Subject 1, 50-lO0 Hz for Subject 2, 3, and 4) power was extracted by averaging this frequency band of the nonnalized time-frequency data.
D. Feature Extraction and Classification The high-gamma time-series data were averaged across the single-trial ongoing movement period to extract suitable features from hand grasping and elbow flection tasks. These averaged data from three to five electrodes in sensorimotor area were used for features. For movement type recognition based on sensorimotor high-gamma signals, we performed linear support vector machine (SVM) analysis. We chose this algorithm because we focused on the spatial (multi dimensional data) distribution of high-gamma power. Classification performances were evaluated by five-fold cross validation. Features were randomly divided into five subgroups, and four of the subgroups were used for training classifier. Then we tested classification perfonnance using remaining subgroups. This procedure was repeated five times and the accuracies were averaged.
series in this electrode. Fig. 1 shows the temporal dynamics of single-trial high-gamma power (blue) and EMG temporal dynamics (red) during hand grasping tasks. We confumed that the high-gamma activation can be easily detected at the single trial condition. Although the onset/offset timing of high gamma power and EMG increases were virtually the same, the temporal activation patterns of high-gamma powers were slightly different from those of EMG dynamics. That is, the peak time points of both high-gamma and EMG were generally the same; however, additional prominent high-gamma peaks were shown near the onset and offset points. Next, we tested whether the two different movement types can be classified by using single-trial sensorimotor high gamma power during movement with high-performance. Since the sensorimotor high-gamma power patterns depending on the movement types were spatially different, averaged multi channel high-gamma power in this area were used as features. Fig. 2 shows the mean classification accuracies between two movement types across all sessions and subjects using SVM. The overall accuracy was 76.28 %. Accuracies in Subject 3 were relatively low because the electrodes did not cover the sensorimotor hand or arm areas and only 1 or 2 electrodes were located on MI.
o
10
20
40 30 Time (5)
50
Figure I. Time-series of single-trial high-gamma power (blue) and EMG data (red) in sensorimotor hand area during repetitive hand grasping tasks. Results shows that sensorimotor high-gamma power is tightly correlated with the trace of hand movement ..
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