EEG based automatic left-right hand movement classification

AbstractObjectives:It is unclear whether the primary motor cortex (PMC) is involved in the mental simulation of movement [i.e., motor imagery (MI)]. The present study aimed to clarify PMC involvement using a highly novel adaptation of the hand laterality task (HLT).Methods:Participants were administered single-pulse transcranial magnetic stimulation (TMS) to the hand area of the left PMC (hPMC) at either 50 ms, 400 ms, or 650 ms post stimulus presentation. Motor-evoked potentials (MEPs) were recorded from the right first dorsal interosseous via electromyography. To avoid the confound of gross motor response, participant response (indicating left or right hand) was recorded via eye tracking. Participants were 22 healthy adults (18 to 36 years), 16 whose behavioral profile on the HLT was consistent with the use of a MI strategy (MI users).Results:hPMC excitability increased significantly during HLT performance for MI users, evidenced by significantly larger right hand MEPs following single-pulse TMS 50 ms, 400 ms, and 650 ms post stimulus presentation relative to baseline. Subsequent analysis showed that hPMC excitability was greater for more complex simulated hand movements, where hand MEPs at 50 ms were larger for biomechanically awkward movements (i.e., hands requiring lateral rotation) compared to simpler movements (i.e., hands requiring medial rotation).Conclusions:These findings provide support for the modulation of PMC excitability during the HLT attributable to MI, and may indicate a role for the PMC during MI. (JINS, 2017,23, 185–193)

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Observation of activation brain areas in response to the stimulation by the right hand movement of Kawahira method using fMRI

2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) ◽

10.1109/nssmic.2012.6551821 ◽

2012 ◽

Author(s):

Takahiko Yokote ◽

Katsuhiro Kida ◽

Satoshi Kuwahara ◽

Takushi Kawashima ◽

Tomoka Doi ◽

...

Keyword(s):

Hand Movement ◽

Brain Areas ◽

Right Hand ◽

The Right

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Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v5i1.pp92-101 ◽

2015 ◽

Vol 5 (1) ◽

pp. 92 ◽

Cited By ~ 1

Author(s):

A. B. M. Aowlad Hossain ◽

Md. Wasiur Rahman ◽

Manjurul Ahsan Riheen

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Probabilistic Neural Network ◽

Hand Movement ◽

Hand Movements ◽

Discrete Wavelet ◽

Eeg Signals ◽

Beta Band ◽

Right Hand ◽

Left And Right

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features. The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

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