Chi-Hong Wang

EEG-Based Emotion Recognition in Music Listening

Ongoing brain activity can be recorded as electroen-cephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% ± 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.

Support vector machine for EEG signal classification during listening to emotional music

An approach to recognize the emotion responses during multimedia presentation using the electroencephalogram (EEG) signals is proposed. The association between EEG signals and music-induced emotion responses was investigated in three factors, including: 1) the types of features, 2) the temporal resolutions of features, and 3) the components of EEG. The results showed that the spectrum power asymmetry index of EEG signal was a sensitive marker to reflect the brain activation related to emotion responses, especially for the low frequency bands of delta, theta and alpha components. Besides, the maximum classification accuracy was obtained around 92.73% by using support vector machine (SVM) based on 60 features derived from all EEG components with the feature temporal resolution of one second. As such, it will be able to provide key clues to develop EEG-inspired multimedia applications, in which multimedia contents could be offered interactively according to the userspsila immediate feedback.

Multilayer perceptron for EEG signal classification during listening to emotional music

In this study an electroencephalography (EEG) signal-based emotion classification algorithm was investigated. Several excerpts of emotional music were used as stimulus for elicitation of emotion-specific EEG signal. Besides, the hemispheric asymmetry alpha power indices of brain activation were extracted as feature vector for training multilayer perceptron classifier (MLP) in order to learn four targeted emotion categories, including joy, angry, sadness, and pleasure. The results demonstrated that the average classification accuracy of MLP could be 69.69% in five subjects for four emotional categories, which is much higher than chance probability of 25%.

Studies of Chinese Original Quiet Sitting by Using Functional Magnetic Resonance Imaging

Since different meditations may activate different regions in brain, we can use functional magnetic resonance imaging (fMRI) to investigate it. Chinese original quiet sitting is mainly one kind of traditional Chinese meditation. It contains two different parts: a short period of keeping phrase and intake spiritual energy, and a long period of relaxation with no further action. In this paper, both those two stages were studied by fMRI. We performed two different paradigms and found the accurate positions in the brain. The pineal gland and the hypothalamus showed positive activation during the first and second stages of this meditation. The BOLD (blood oxygenation level dependent) signal changes had also been found

Comparison of fMRI BOLD Effect and Arterial Pulsation Harmonic Distribution among Different Breathing Rate

The phenomenon of respiratory sinus arrhythmia (RSA) had been studied since 1733. Most researches were focused on the heart rates or blood pressure variability. It was well known that heart rate variability (HRV) induced by respiration decreased progressively with age. In general, it is caused by the function of the autonomic nervous system (ANS). Seldom researches studied the relationship of this phenomenon with cerebral circulation. In our previous research, we found that different breathing rate could redistribute the proportion of systemic circulation, and also observed that the slower the breathing rate the more proportion of cerebral circulation appeared on head. In this paper, we further examined the BOLD (Blood Oxygenation Level Dependent) signal fluctuations in brain stem among different breathing rate by the technique of functional magnetic resonance imaging (fMRI). We found that the BOLD signal changes were hinged on the breathing rate, and the variability was consistent with the pulsatile pressure study.

Buddhist Meditation: An fMRI Study

Since different meditation methods may activate different regions in brain. In this study we chose a basic meditation type that just practiced the breath with the phrase that contained nine words, and the first word was matched with inhaling and following the next with exhaling alternately during the meditation period. Blood-oxygenation-level-dependent (BOLD) based fMRI were used to examine the brain functions. Experiments showed brain activation areas on the region of thalamus, anterior cingulate, posterior cingulate, middle temporal gyrus, as well as putamen and other activations. It displayed that meditation practice concerned with cognitive functions, however, hypothalamus could be activated during meditation practice, and that might be related with endocrine secretion. But it requires further researches combined BOLD-and CBF-based fMRI technique and physiological signal detection simultaneously to explore the mechanism of meditation.

Forced and Non-forced Chinese Meditation Studies

There exist many different types of meditation. The mechanism why meditation improves people’s health remains unclear. Since different meditations may activate different regions in brain, we can use functional magnetic resonance imaging (fMRI) to investigate it. We may simply divide meditation into two different types, which is forced meditation (FM) and non-forced meditation (NFM). The FM type may keep a phrase in mind, observe breathing, proceed mind imaging or any other actions to keep people’s attention focused. Chinese original quiet sitting (COQS) is mainly one kind of traditional Chinese meditation. It contains two different parts: a short period of keeping phrase and receiving spiritual energy, and a long period of relaxation with no further action. The second part of it is really what we called “non-forced” type, whereas the first part is normally a forced one. In this paper, we want to find out the brain activation area precisely during FM and NFM. We studied the observing breathing meditation (OBM) and the first part of COQS as the FM type, and also studied the second part of COQS as NFM type. Our experimental results showed very different activation patterns among the brain between FM and NFM. The BOLD (Blood Oxygenation Level Dependent) signal changes had also been found. The posterior cingulated gyrus showed strong activation in OBM, the pineal gland and hypothalamus showed positive activation in the first and second stage of COQS separately. From our results, we found the basic meaning of the mechanism why meditation improves people’s health.

The clinical engineering program in NTUH

Clinical engineering is an interdisciplinary science. It has several aspects in its interfacing practices among healthcare delivery system that include (1) specific engineering supports to clinical specialties, (2) hospital system engineering programs, (3) managerial integration based on biomedical engineering practices, (4) safety and performance assessment of medical devices, and (5) compliance with specific regulations and standards. Each aspect requires highly integrated interdisciplinary principles to fulfill its professionalism. These are different professions based on academic biomedical engineering principles and healthcare principles. This clinical engineering practical training program is designed to fulfill the requirement of human resources for the advanced biomedical engineering applied and practised in hospital environments.