Kyung-Jin You

Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

We provide a novel method to infer finger flexing motions using a four-channel surface electromyogram (EMG). Surface EMG signals can be recorded from the human body non-invasively and easily. Surface EMG signals in this study were obtained from four channel electrodes placed around the forearm. The motions consist of the flexion of five single fingers (thumb, index finger, middle finger, ring finger, and little finger) and three multi.finger motions. The maximum likelihood estimation was used to infer the finger motions. Experimental results have shown that this method can successfully infer the finger flexing motions. The average accuracy was as high as 97.75%. In addition, we examined the influence of inference accuracies with the various arm postures.

A new direction-of-arrival estimation method using automotive radar sensor arrays:

This article presents a new signal processing method of estimation of direction of arrival using the phase difference between sensors. The new method is essentially a narrow-band technique for automotive radar. A delay of the reception time is caused by the physical gap between sensors in the incident signals to the sensor arrays, and it results in a phase difference between input signals. The new method predicts the ideal phase value and the phase value of the input signal. The point that minimizes the phase error for every sensor is estimated as direction of arrival. The simulation result shows that the new method offers significantly improved estimation resolution and direction-of-arrival estimation compared to conventional methods.

Spectral Gini Index for Quantifying the Depth of Consciousness

We propose indices that describe the depth of consciousness (DOC) based on electroencephalograms (EEGs) acquired during anesthesia. The spectral Gini index (SpG) is a novel index utilizing the inequality in the powers of the EEG spectral components; a similar index is the binarized spectral Gini index (BSpG), which has low computational complexity. A set of EEG data from 15 subjects was obtained during the induction and recovery periods of general anesthesia with propofol. The efficacy of the indices as indicators of the DOC was demonstrated by examining Spearmans correlation coefficients between the indices and the effect-site concentration of propofol. A higher correlation was observed for SpG and BSpG (0.633 and 0.770, resp., p < 0.001) compared to the conventional indices. These results show that the proposed indices can achieve a reliable quantification of the DOC with simplified calculations.

Performance of odorants inference depending on functional and spatial neuron grouping

This paper presents performance of olfactory neural decoding depending on functional and spatial neural selection. Multi-channel extra-cellular single-unit recording were done by micro-wire electrodes implanted in the mitra/tufted cell layers of the main olfactory bulb (MOB) of anesthetized rates to obtain neural responses to various odors. All neurons are classified according to significant differences response to using t-test (p&#60;0.01). Odor can be robustly inferred by using subpopulations of neurons. The results indicate that the performance of odor inference is highly dependent on the neural selection. Also, we compared different statistical methods correlations of olfactory decoding accuracy. The results show that t-test is better criterion of assessment of neurons capacity than others.

M1 neural decoding of finger movements using a priori neural activities before movements

One of the efforts on brain-machine interfaces (BMI) is reducing the system delay to perform sophisticated movements. Motor commands in brain are produced in advance before its movement in central nervous system. In the field of electroencephalogram (EEG), this issue has been studied widely as readiness potential (RP) to overcome time critical problem. Neural responses prior to movement in M1, primary motor cortex have been observed. In this paper, we present the neural decoding of finger or wrist movements using neural activity data collected from 0.5 sec. to 1.0 sec. when the movement onset happened at 1.0 sec. With novel temporal window, 30 randomly selected neurons could perform competent decoding accuracy as high as 95%.