1. RESULTS AND CONCLUSIONS
References
PSYCHOLOGICAL TASKS
METHODS
Subjects: n= 9 healthy right-handed subjects
(mean age of 28.1 ± 6[SD])
Electroencephalogram (EEG) was recorded using a Mitsar 32 channel EEG sys-tem (Mitsar, Ltd. St. Petersburg, from 19 sites - Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2 (10-20%) with ears linked reference, filtered between 0.53 and 30 Hz, 50 Hz notch filter, sample rate Hz. The ground electrode was placed on the forehead. The electrode impedance was kept at less than 5 kΏ. For EEG analysis was used WinEEG software (Ponomarev, V.A., Kropotov, Ju.D., registration no at ).
The myogramm was recorded to control the motor activities of the fingers during the real and imaginary movements. The electrodes were placed near the base of the proximal phalanx and on the proximal phalanx of the finger executing the respective movement. Later, only the trials with the imagined movements of fingers of one hand without myographic artifacts were used for the analysis.
CLASSIFIERS AND FEATURES
Results of the 1st level classifier (2 best individual channels from the sensorimotor cortex)
Results of the 1st level classifier demonstrated higher accuracy of imaginary finger movements for the length of the curve as a selected feature.
EEG desynchronization (8-12Hz, 18-26Hz) in C3, C3-Cz zones during real movements appears prior to the moment of pressing button and is completed after pressing, which corresponds to the effects described in the literature. Desynchronization of EEG during imagery of the respective finger movements is weak and time shifted relative to the changes during real pressing(no significant effects).
Applied CSD conversion to the EEG signals of sensorimotor and supplementary cortex zones demonstrated average accuracy of four class single trial classification for all subjects 50 ± 7% [SD] for the pair of sites F3–C3 (maximum, 58%) and 46 ± 11% [SD] (maximum 62%) for the pair of sites C3–Cz.
Applied CSD conversion to the EEG signals allowed to increase decoding accuracy of imaginary finger movements as compared to previously obtained results with WAR and ICA EEG signal preprocessing. The average accuracy of four class single trial classification for all subjects with selection of the best channels over the whole cortex was 64±10% (maximum 83%).
Selection of individual windows for signal classification in some subjects increased the accuracy classification. Most informative intervals in most subjects began 200 ms before and were over 200–300 ms after the supposed moment of imaginary movement in applied rhythm paradigm of motor imagination.
The study shows good application of CSD conversion for EEG signals and the possibility of individual approach to the selection of time and spatial parameters for the feature generation in order to improve classification accuracy for some subjects in individual application of BCI.
2. Second level classification accuracy of imaginary finger movements
(2 best individual channels over the whole cortex)
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Sonkin K.M., Stankevich L.A., Khomenko Ju.G., Nagornova Zh.V., Shemyakina N.V.(2015) Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand //Artificial Intelligence in Medicine, v.63, N.2, p
Stankevich L. A, Sonkin K. M. , Shemyakina N. V., Nagornova Zh. V., Khomenko Yu. G., D. S. Perets Koval A. V. (2016 in press) EEG Pattern Decoding of Rhythmic Individual Finger Imaginary Movements of one Hand// Human Physiology, Vol. 42, No. 1, pp. 32–42.
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Classification of EEG Patterns of Finger Imaginary Movements Preprocessed by CSD conversion
Natalia Shemyakina1, Zhanna Nagornova1, Konstantin Sonkin2, Julia Khomenko3, Dmitry Perets2, Alexandra Chevikalova1, Lev Stankevich2
I.M. Sechenov Institute of evolutionary physiology and biochemistry RAS, St. Petersburg, Russia
St. Petersburg State Polytechnical University, St.Petersburg, Russian Federation,
N.P. Bechtereva Institute of Human Brain, Russian Academy of Sciences, St.Petersburg, Russian Federation
INTRODUCTION: An effective and successive classification of EEG patterns is a step towards creation and improvement of mobile, cheap noninvasive brain–computer interface (BCI), that is an actual interdisciplinary problem for the rehabilitation purposes (for review see [1], [2]). Main problems, that have to be solved for elaboration of the effective classifiers & BCIs are the enhancement of decoding accuracy of EEG patterns (exploring new features), increase of speed and degrees of freedom for classification and existing BCI systems ([2];
GOAL: to explore possibility to use current source density (CSD) conversion as the preprocessing procedure, enhancing decoding of EEG patterns of finger imaginary movements on base of previously elaborated scalable committee of classifiers [3,4].
Key words: - imagery finger movements, EEG, CSD, WAR, ICA, heterogeneous classifier, ANN, SVM
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The subjects performed four types of motor imagery tasks for the fingers of the right hand (thumb, index, middle and little fingers) with the eyes opened. They have to follow the rhythm of imagination previously set by sounds (Fig.1). The tasks were performed following a block design. Each block consisted of the two types of trials: acoustic or synchronizing trials, when the subjects had to press the mouse button simultaneously with the presented sounds, and the trials on imagination of the same movements but without sound presentation. Within a single task block, the series of real and imaginary movements were repeated many times. As a result, the subject executed not less than 70 real and 120 imaginary movements in the specified rhythm within each task block. Each single trial lasted for 600 ms with fixed 100 ms intervals in-between trials (intersound/interimaginary periods ms). The order of task blocks was randomized between subjects.
We adhered to the conception that execution of the real and the respective imaginary movements took approximately the same time [5-6]. The instruction for motor imagery was aimed at initiating kinesthetic feelings in a subject [7].
Features:
The neurological committee of
heterogeneous classifiers
Two- level scalable neurological committee of classifiers on the basis of support vector machines(SVM) and artificial neural networks(ANNs) was used for classification.
Steps to EEG classification
Corresponding author:
WAR
Fig. 1. The scheme of a task block. White rectangles with a loud speaker are synchronizing trials with real pressings; white rectangles with a crossed loud speaker are trials with pressing imagination; gray rectangles are trials excluded from the analysis, when pressings were done by inertia or, on the contrary, were not done when changing the types of trials. The arrows indicate pressing in the synchronizing trials.
ANNs were constructed as a multilayered perceptron with two input layers and one output layer. The sigmoid activation (hyperbolic tangent) function was used in the formal neurons of input layers and the linear function was used in the neurons of output layer.
The network training process continued until reaching the specified classification accuracy for the entire training sample. The artificial neural network was trained once for each type of movement. The training sample included 70% trials and the test sample
included the following 30% trials of the sample formed. The work of the first level classifiers resulted in the generation of a feature vector to input into a generalizing artificial neural network of the second level.
Support: RFBR _ofi-m
EEG under WAR/ICA or CSD [8-10] conversion were used to calculate the two types of features: the area under the signal curve and the curve length calculated in the sliding windows of analysis (the window length – 100 ms; the shift – 50 ms).
Statistical data analysis was carried by means of ANOVA/MANOVA and non-parametric approaches
EEG data from sensorimotor cortex and supplementary areas of the left hemisphere was used for classification,
also by means of preliminary classification mapping were revealed individual best channels in the sensorimotor and whole cortex
EEG data
S I N G L E – T R I A L C L A S S I F I C A T I O N R E S U L T S
3. Exploring individual informative intervals
Averaged EEG power desynchronization during the real and imaginary movements of 4 fingers of one hand.
The heavy line - power changes during real pressing; the fine line - changes during imaginary pressing. The vertical dashed lines - show the time interval of sound presentation in the trials with real pressings and the respective time interval in the absence of sound in the trials with imaginary movements.
The wavelet transform [Morlet as in 11] was calculated in the range of 4–30 Hz with 1Hz steps, with a five cycle width of the wavelet.
Fig.2 illustrates significant 22Hz (18–26 Hz) changes in group of subjects.
Significant influence of TIME INTERVAL factor on decrease of power was also obtained in 8-12Hz (C3 zone, F(5,35) = 4.6, p < 0.05).
RT in synchronizing trials on average was 365±10.4 [SD] (ms).
Finger movements being relatively synchronous with sound termination in the synchronizing trials supported the idea of possible finger movement imagination with
the corresponding times
Behavioural data
Subjects
Accuracy %
ICA, WAR, CSD
For both 1st level classifiers (SVM with radial basic function and ANN) length of the curve demonstrated higher accuracy while discriminated as a selected feature. Dotted line – theory guess level (25%)
ANOVA(factor - conversion type), F=3.7, p<0.05. post-hoc analysis demonstrated higher classification accuracy with implementation of CSD in comparison with ICA (p<0.03) and WAR (p<0.05) while imaginary finger movements. In case of individual data it could be seen that WAR results are rather comparable. Group mean accuracy of 4-class imaginary finger movements classification: CSD - 64±10%; WAR - 60±10% ICA - 59±9%
Table 1. The accuracy of 4-class classification of imaginary finger movements of one hand in different intervals of the trial
The results of classification by the most informative intervals demonstrate the possibility of improving its accuracy compared to classification by the entire duration of the trial for two subjects (Sub. 1, z = –4.9, p < ; Sub. 6, z = –3.5, p < , the Mann–Whitney U test). In other subjects, classification accuracy changed insignificantly. In other cases results are comparable
In common could be concluded, that classification of EEG patterns of imaginary finger movements demonstrated increase of classification accuracy while selection of channels, type of raw data conversion and time intervals. Individual approach is the promising application for the EEG pattern classifications
during the fine motor imagery
For classification was taken EEG signal 1.5 Hz-30 Hz
By means of single-trial approach
F(5,35) = 6.4, p < 0.01
F(5,35) = 5.4, p < 0.05
Fig.2
Fig.1