Yodchanan Wongsawat

Hybrid EEG-EOG brain-computer interface system for practical machine control

Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

Palmprint image enhancement using phase congruency

This paper proposes the use of the phase congruency to enhance the palmprint image used for palmprint identification. By using phase congruency, the palmprint lines which act like the edges in the palmprint image are detected. The resulting phase image is shown as the enhanced palmprint image. Comparing with the previous palmprint enhancement method that uses the phase symmetry proposed by Kovesi et al. (1997), the proposed method is significantly less sensitive to the textures which are not the palmprint lines in the palmprint image.

Motion visual stimulus for SSVEP-based BCI system

Steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) system is one of the most accurate assistive technologies for the persons with severe disabilities. However, the existing visual stimulation patterns still lead to the eyes fatigue. Therefore, in this paper, we propose a novel visual stimulator using the idea of the motion visual stimulus to reduce the eyes fatigue while maintaining the merit of the SSVEP phenomena. Two corresponding feature extractions, i.e. 1) attention detection and 2) SSVEP detection, are also proposed to capture the phenomena of the proposed motion visual stimulus. Two-class classification accuracy of both features is approximately 80%, where the maximum accuracy using the attention detection is 90%, and the maximum accuracy using the SSVEP detection is 100%.

Hybrid SSVEP-motion visual stimulus based BCI system for intelligent wheelchair

This paper proposes the hybrid BCI modalities for wheelchair control by taking into account weakness of the current BCI systems. The idea is to combine two hybrid BCI systems with the intelligent wheelchair for three states, i.e. normal, fatigue, and emergency states. First system is the hybrid steady state visual evoked potential (SSVEP) and alpha rhythm BCI which is designed to use in the normal state. Second system is the hybrid motion visual stimulus and alpha rhythm which can be employed during the fatigue state (after using the first system). For the experiment, subjects are asked to perform SSVEP system for 30 minutes (until the fatigue states occur). Then, the subjects will be asked to perform the hybrid motion visual stimulus and alpha rhythm testing. The accuracy of the proposed system during fatigue state is approximately 85.62%. With this idea, BCI controlled wheelchair can be efficiently employed in reality.

EEG brain mapping and brain connectivity index for subtypes classification of attention deficit hyperactivity disorder children during the eye-opened period

Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent neurological disorders. It is classified by the DSM-IV into three subtypes, i.e. 1) predominately inattentive type, 2) predominately hyperactive-impulsive type, and (3) combined type. In order to make the treatment via the neurofeedback or the occupational therapy, quantitative evaluations as well as ADHD subtype classification are the important problems to be solved to enhance an alternative way to treat ADHD. Hence, in this paper, we systematically classify all of these three subtypes by the 19-channel EEG data. Three brain mapping (QEEG) techniques, i.e. absolute power of frequency bands, coherence, and phase lag, are employed to visualize each type of the ADHD. ADHD children with combined type have deficit in delta theta and alpha activity. For the inattentive type, there are excessive delta and theta absolute power in the frontal area as well as the excessive coherence in beta and high beta frequency bands. For the hyperactivity and impulsive type, the behavior is dominated by the slow wave. This information will give benefits to the psychiatrist, psychologist, neurofeedback therapist as well as the occupational therapist for quantitatively planning and analyzing the treatment.

Semi-automatic P300-based brain-controlled wheelchair

The self mobility is a dream of many persons who suffered with disability. To realize their hope, brain-controlled wheelchair (BCW) will be a powerful system for mobility improvement. P300 is the reliably brain phenomenon that can be used for interpreting brain commands as well as eyes-blink artifact signal. This paper aims to propose a prototype of BCW that allows the persons with severe disabilities to practically control the electric wheelchair in their home environment. The combination of EEG signal between P300 phenomenon and eyes-blink artifact were used as a hybrid BCW system. Furthermore, this hybrid BCW can be operated in automatic navigation and normal control mode. These two modes delivered 4 destination commands in automatic navigation control and 4 direction commands (forward, backward, turn left and turn right) in normal control mode. These commands were selected via P300 processing system. The different pattern of eyes blinking was used for fast stop, on/off P300 system, and mode changing command (2 times, 3 times and 4 times of eyes-blinking respectively). The result show that the prototype BCW can be operated both automatic navigation and brain control mode with safe by 2 times confirmation of P300 command. Without assistant, user can operate this system by them self via defined different pattern of eyes blinking command. 100% accuracy can be achieved in eyes-blinking detection algorithm. With our new design of LED based P300 stimulator, 95% averaged accuracy with the transfer rate of 3.52command/minute can be achieved by simple and timesaving P300 detection algorithm.

Wireless sensor network-based smart room system for healthcare monitoring

In this paper, the utilization of Zigbee as wireless sensor network (WSN) for medical application is demonstrated. The combination of various topologies is used to configure wireless sensors network to achieve high efficiency network architecture in medicine. The network consists of center coordinator, routers and sensor nodes. Mesh network is used for the connection between coordinator and router for range expansion. A performance of the proposed modality is tested in the normal situation. Besides, the architecture of the smart room systems is also proposed for healthcare monitoring. Physiological data and signal are transmitted using Xbee which is a wireless device operated in unlicensed radio frequency bands.

Effects of LED-backlit computer screen and emotional selfregulation on human melatonin production

Melatonin is a circadian hormone transmitted via suprachiasmatic nucleus (SCN) in the hypothalamus and sympathetic nervous system to the pineal gland. It is a hormone necessary to many human functions such as immune, cardiovascular, neuron and sleep/awake functions. Since melatonin enhancement or suppression is reported to be closely related to the photic information from retina, in this paper, we aim further to study both the lighting condition and the emotional self-regulation in different lighting conditions together with their effects on the production of human melatonin. In this experiment, five participants are in three light exposure conditions by LED backlit computer screen (No light, Red light (~650 nm) and Blue light (~470 nm)) for 30 minute (8-8:30 pm), then they are collected saliva both before and after the experiments. After the experiment, the participants are also asked to answer the emotional self-regulation questionnaire of PANAS and BRUMS regarding each light exposure condition. These results show that positive mood mean difference of PANAS between no light and red light is significant with p=0.001. Tension, depression, fatigue, confusion and vigor from BRUMS are not significantly changed while we can observe the significant change in anger mood. Finally, we can also report that the blue light of LED-backlit computer screen significantly suppress melatonin production (91%) more than red light (78%) and no light (44%).

The effects of perceiving color in living environment on QEEG, Oxygen saturation, pulse rate, and emotion regulation in humans

Light and color have been shown to have substantial physical, psychological and sociological effects on humans. Hence, an investigation on the effect of changes in light and color to the biological signals is a challenging problem. Five participants were measured the oxygen saturation (SpO2), pulse rate, and quantitative electroencephalogram (QEEG) in six colors (white, blue, green, yellow, red and black) of living environment for 5 minutes per color. Then all participants were asked to answer the emotional questionnaire of BRUMS and color performance for each color environment. The results showed brain activity of high beta wave (25-30 Hz) that associated with alertness, agitation, mental activity, and general activation of mind and body functions (at frontal lobes and temporal lobes) in red and yellow colored rooms were higher than blue, green, white and black colored rooms, respectively. It also had the relationship with the psychological effect (BRUMS). The amplitude asymmetry of beta wave (12-25 Hz) was highly attenuated in warm color (red and yellow colored rooms), moderately attenuated in cool color (green and blue colored room) and little attenuated in white and black colored rooms. The BRUMS showed that red and yellow yielded significant effect on anger (F=4.966, p=0.002) and confusion (F=3.853, p=0.008). Red and green color yielded high effect on vigor. Green color did not affect the depression. Blue color yielded moderate effect on confusion, tension and fatigue. White and black colors yielded low effect on any mood, but black color had no effect on vigor. In addition, we cannot observe any significant changes of pulse rate and blood oxygen saturation in each color. The results can possibly be used as the recommendation to design the room for either normal people or patients.

Wireless-based portable EEG-EOG monitoring for real time drowsiness detection

Drowsiness is one of the major risk factors causing accidents that result in a large number of damage. Drivers and industrial workers probably have a large effect on several mishaps occurring from drowsiness. Therefore, advanced technology to reduce these accidental rates is a very challenging problem. Nowadays, there have been many drowsiness detectors using electroencephalogram (EEG), however, the cost is still high and the use of this is uncomfortable in long-term monitoring because most of them require wiring and conventional wet electrodes. The purpose of this paper is to develop a portable wireless device that can automatically detect the drowsiness in real time by using the EEG and electrooculogram (EOG). The silver (Ag) conducting fabric consolidated in a headband used as dry electrodes can acquire signal from the users forehead. The signal was sent via the wireless communication of XBee® 802.15.4 to a standalone microcontroller to analyze drowsiness using the proposed algorithm. The alarm will ring when the drowsiness occurs. Besides, the automatic drowsiness detection and alarm device yields the real-time detection accuracy of approximately 81%.