Blind Source Separation Based Analysis of Movement Related Cortical Potentials

Akane Sano1, Bakardjian Hovagim2, Andrzej Cichocki2, Yutaka Tomita1 1 Graduate School of Science and Technology, Keio University,

3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, JAPAN 2 Riken Brain Science Institute,

2-1 Hirosawa, Wako, Saitama, 351-0198, JAPAN sano@bme.bio.keio.ac.jp

Abstract: We compared EEG responses during various types of voluntary movements, i.e., self-paced and cue-guided, single and rhythmic, and actual and imaginary movements. We applied second-order statistics based blind source separation for de-noising. Keywords: Electroencephalogram (EEG), Movement Related Cortical Potentials (MRCP), Motor- Control, Blind Source Separation (BSS)

1. Introduction

Motor control and movement are the most important actions in our daily livings. Cortical activation related to voluntary movements has been researched for a long time, especially from the point of motor coordination; Kelso et al. have investigated EEG responses during synchronization and syncopation to auditory cues [1], especially focused on the transition from syncopation to synchronization. The relationship between the transition only in synchronized coordination and EEG responses has not been researched yet.

In our research, we focused on types of movements; single, rhythmic and imaginary movements or cue-guided and self-paced movements. Our objective is to compare electroencephalogram (EEG) corresponding to the movement types. In addition, recently, measurement system on brain has been progressed and at the same time, a new procedure to analyze the data is also developed. Blind source separation (BSS) is one of the signal processing techniques, based on statistical values. We applied AMUSE based on second order statistics for EEG responses during movements using a 64 channel high density EEG system.

2. Experimental Procedures The experiments were performed on 6 right-handed

participants without a history of neuromuscular disorders. A 64-channel high-density array EEG System (NeuroScan, Compumedics, Abbotsford, Australia) was used for measuring EEG based on the extension of 10-20 International System. The same system was also use to acquire the surface-EMG of the right wrist (m. extensor indicis). The sampling frequency was 1 kHz. Participants seated on a comfortable chair and were given 3 experiments.

2.1 Experiment1 – Single Tapping

Self paced movement Participants tapped a button using an index finger

without any timing cues.

Random Cue-guided movement Participants were given auditory cues; however these

cues were not rhythmic, but randomly occurred.

2.2 Experiment2–Rhythmic Tapping

Participants were given tapping movement tasks described below. A total of 15 conditions, composing of 3 tasks below

1. Auditory cue-guided movement => self-paced movement

2. Auditory stimulation for learning rhythms => self-paced movement => cue-guided movement

3. Self-paced movement

Above tasks were carried out at 5 tapping rates (0.2, 0.4, 1, 2.5 and 4 Hz).

2.3 Experiment3–Actual and Imaginary Movements

Participants were given auditory cues to perform actual and imaginary movements alternatively of bending hand joints.

3. Blind Source Separation Blind source separation is a statistical technique for

decomposing a complex dataset into uncorrelated (in spatial

temporal decomposition) or independent sub-parts (in independent components analysis)[2].

Considering the classical BSS model with instantaneous

mixing Asx = (Eq.(1)), where 1 2T

m=x [ , , ..., ]x x x are

observations, the sources 1 2T

m=s [ , , ..., ]s s s are mutually

uncorrelated or independent and A is the mixing matrix. The goal is to find only from the observations x , a matrix W such that estimated source Wxy = (Eq.(2)) is an estimate of the possibly scaled and permutated source vector s .

Many algorithms have been developed for BSS, however in our research; we used AMUSE (Algorithm for Multiple Unknown Signals Extraction) based on second order statistics with fast computational time. The concept of AMUSE is described in [3].

In analysis using BSS, we used ICALAB (MATLAB based software developed by Lab. for Advanced Brain Signal Processing, Riken Brain Science Institute). In this research, we extracted useful separated signals based on 70 % contribution rate of eigenvalues obtained eigenvalues in the calculation of singular value decomposition in AMUSE algorithm.

4. Results

4.1 BSS Performance

Figure 1 showed the comparison between the BSS applied signals and the conventional band-pass filtering signals in 0.4 Hz of the former part of task1 on C3. The BSS applied signal has almost the same envelope as the band-pass filtered signals. The BSS applied signal is smoother than conventional ones. We drew the topographical maps of the BPF and the BSS applied signals (Figure 2). Negative potential about 850 ms prior to the movement onset (0 ms) was seen in left central region around the motor area. As the progress of time, the potential changed into positive. The potential of C3 area is the characters of the movement related cortical potentials. BSS applied map had more localized potentials than the conventional BPF method and BSS seemed to eliminate the far field potentials like Laplacian filtering.

Fig.1. Comparison between BSS applied signal (solid line) and band-pass filtered signal (dotted line)

(a) Band Pass Filtering

(b) Blind Source Separation

Fig.2. Comparison of topographical maps by 100 ms between conventional method and BSS method

4.2 EEG Responses

4.2.1 Self-paced and Randomized Cue-guided Movement

Figure 3 showed 30 times synchronized-averaged data on C3. In readiness potentials, self-paced task had about 900 ms of latency of readiness potential. Randomized cue-guided task had about 50 ms

Fig.3. Thirty times averaged EEG of self-paced movement (blue) and randomized cue-guided movement (purple) on C3

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4.2.2 Rhythmic Tapping

In Fig. 4 we showed topological maps by 100 ms between��2500 and 2500 ms in 0.2 Hz self-paced tapping of task 1, 2 and 3. Readiness potentials generated about �1000, �1200 and �1200 ms prior to the movements. In addition, in Table 1 latency and amplitude of readiness potentials in 0.4 Hz were summarized. (a)

(b)

(c)

Fig.4. Topographical maps in self-paced movements of Task 1(a), 2(b) and 3(c)

Table 1 Latency and Amplitude of Readiness Potential

Latency [ms] Peak [µV]

Cue -790±26 4.78±1.68 Task1

Self -523±64 3.29±0.67

Cue -542±143 3.78±1.44 Task2

Self -705±62 5.28±0.94

Task3 Self -835±87 6.76±0.48

4.2.3 Imaginary Movements

In Fig. 5, 30 times synchronized-averaged actual and imaginary movements (as for actual movements, both EMG and sound triggered EEG are shown) were shown. Figure 6 were topographical maps between -2500 and 2500 ms by 100ms in actual and imaginary movements triggered by sounds.

Fig.5. Comparison between actual and imaginary movements (Green: Actual movement triggered by EMG onset, Red: Actual triggered by sound onset, Blue: Imaginary movement triggered by sound onset)

5. Discussions

5.1 Cue-guided and Self-paced Movements

Randomized cue and self-paced movements indicated remarkable difference. The former had steeper readiness potential with delayed latency than the latter. In randomized cue-guide movements, all participants tapped after listening to the cue. In self-paced movements, movement related cortical potential was generated earlier; this was because supplementary motor cortex and premotor cortex were more activated in self-fixed rhythms [4].

5.2 Single and Rhythmic Movements

As progress of the tapping time, amplitudes of readiness potentials decreased and latencies were delayed.

Compared between rhythmic tapping, slower movements had larger readiness potentials and motor potentials.

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Considering earliness or lateness of time, compared within each task, the former tasks (in self-paced, the middle part of task2 and in cue-guided, the former of task1) had larger and earlier readiness potentials.

As for difference in the same types of tapping, earliness and amplitude of readiness potential are ordered task3 > task2 > task1 in self-paced, and task1 > task2 in cue-guided, however the difference was remarkable in self-paced movements.

Results of comparison between tasks, the relationship between large amplitude and early latency of readiness potentials, and decreased and delayed readiness potentials as progress of time were observed.

(a) Actual Movement

(b)Imaginary Movement

-5 5 [µV] Fig.6. Topographical maps between actual and imaginary

movements between –2500 and 2500 ms by 100 ms (Vertical black bar means 0 ms, the onset of sounds)

5.3 Actual and Imaginary Movements

As Fig.3 shows, after 0ms, polarity of EEG was opposite. In addition, activations on areas around SMA and MI could be observed clearly even in imaginary movements. Traditionally, it has been considered SMA has the function for the preparation of movements and MI for

execution, however recently they are not independently functioned [5]. In our results, even in imaginary movements SMA and MI were both activated. Especially, the area around SMA was activated earlier than that around MI. In addition, the negativity spread into frontal cortex. Combined potentials in imaginary movements using fast analyzing method BSS, it can be applied for Brain Computer Interface.

6. Conclusions

In this research, we measured EEG responses during cue-guided and self-paced, single, rhythmic and imaginary tapping movements and compared brain activities using second-order statistics based spatial temporal decomposition into EEG response for de-noising. We observed the relationship between larger amplitude and early latency of readiness potentials. Also, as the progress of time, readiness potentials decreased and delayed. In addition, the imaginary movements had opposite polarity to the actual ones, but similarity in activations around both MI and SMI were observed.

References [1] C. Babiloni et al., Human movement-related potentials

vs desynchronization of EEG alpha rhythm: a high-resolution EEG study”, Neuroimage. Vol.10, No.6, pp.658-65, 1999

[2] A. Cichocki, S. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications, John Wiley & Sons Inc, 2002

[3] L. Tong et al., Indeterminacy and identifiability of blind identification, IEEE Trans. CAS, Vol. 38, pp. 499-509, 1991.

[4] S. K. Jankelowitz et al., Movement-related potentials associated with self-paced, cued and imagined arm movements, Exp Brain Res., 147, pp. 98-107, 2002

[5] M. Lotze et al., Activation of Cortical and Cerebellar Motor Areas during Executed and Imagined Hand Movements: An fMRI Study, J Cog Sci, Vol.11, No.5, pp.491-501, 1999

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