• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.774-779
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• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).

• Physical Therapy Rehabilitation Science
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• v.4 no.1
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• pp.28-31
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• 2015

Objective: Psychomotor imagery has been widely used to improve motor performance and motor learning. Recent research suggests that during visualization, changes occur in neurophysiological networks that make physical practice more effective in configuring functional networks for skillful behaviors. The aim of our pilot study was to determine if there was change and to what extent there was differentiation in modulation in electroencephalography (EEG) frequencies between visualizing a state of rest and a state of exercise performance and to identify the preponderant frequency. Design: Quasi-experimental design uncontrolled before and after study. Methods: EEG brain wave activity was recorded from 0-40 Hz from nine cerebral cortical scalp regions F3, Fz, F4, C3, Cz, C4, P3, POz, and P4 with a wireless telemetric EEG system. The subjects, while sitting on a chair with eyes closed, were asked to visualize themselves in a state of routine rest/relaxation and after a period of time in a state of their routine exercise performance. Results: The gamma frequency, 31-40 Hz, (${\gamma}$) was the predominant wave band in differentiation between visualizing a state of rest versus visualizing a state of exercise performance. Conclusions: We suggest these preliminarily findings show the EEG electrocortical activity for athletes is differentially modulated during visualization of exercise performance in comparison to rest with a predominant ${\gamma}$ wave band frequency observed during the state of exercise. Further controlled experimental studies will be performed to elaborate these observations and delineate the significance to optimization of psychomotor exercise performance.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.265-272
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• 2020

In the research of brain computer interface (BCI) technology, one of the big problems encountered is how to deal with some people as called the BCI-illiteracy group who could not control the BCI system. To approach this problem efficiently, we investigated a kind of spectral EEG characteristics in the prior resting state in association with BCI performance in the following BCI tasks. First, spectral powers of EEG signals in the resting state with both eyes-open and eyes-closed conditions were respectively extracted. Second, a convolution neural network (CNN) based binary classifier discriminated the binary motor imagery intention in the BCI task. Both the linear correlation and binary prediction methods confirmed that the spectral EEG characteristics in the prior resting state were highly related to the BCI performance in the following BCI task. Linear regression analysis demonstrated that the relative ratio of the 13 Hz below and above the spectral power in the resting state with only eyes-open, not eyes-closed condition, were significantly correlated with the quantified metrics of the BCI performance (r=0.544). A binary classifier based on the linear regression with L1 regularization method was able to discriminate the high-performance group and low-performance group in the following BCI task by using the spectral-based EEG features in the precedent resting state (AUC=0.817). These results strongly support that the spectral EEG characteristics in the frontal regions during the resting state with eyes-open condition should be used as a good predictor of the following BCI task performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.429-434
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• 2012

This paper presents a technology for manipulating external devices by Brain Computer Interface (BCI) system. Recently, BCI based rehabilitation and assistance system for disabled people, such as patient of Spinal Cord Injury (SCI), general paralysis, and so on, is attracting tremendous interest. Especially, electroencephalogram (EEG) signal is used to organize the BCI system by analyzing the signals, such as evoked potential. The general findings of neurophysiology support an availability of the EEG-based BCI system. We concentrate on the event-related synchronization of motor imagery EEG signal, which have an affinity with an intention for moving control of external device. To analyze the brain activity, short-time Fourier transform and particle swarm optimization are used to optimal feature selection from the preprocessed EEG signals. In our experiment, we can verify that the power spectral density correspond to range mu-rhythm(${\mu}8$~12Hz) have maximum amplitude among the raw signals and most of particles are concentrated in the corresponding region. Result shows accuracy of subject left hand 40% and right hand 38%.