JOB SATISFACTION AMONG ACADEMIC STAFF IN THE PUBLIC UNIVERSITIES OF THAILAND

This study focused on job satisfaction in various aspects: day – to – day activities, work environment, compensation, and communication. In addition, the study investigated the factors leading to potential problems such as absenteeism and turnover. All participants were from Social Sciences Cluster academic staff aged between 25 to 60 years old in the public universities situated at the lower northern region of Thailand. The survey questionnaire was applied as research instrument to collect the quantitative data. The findings revealed that the overall job satisfaction of the academic staff was high. Day – to – day activities was ranked as the highest and compensation was ranked as the lowest. Additionally, the most influential factors that led to the problem at work belonged to work overload, lack of communication and lack of professional growth and development, respectively.&nbsp

Doctoral Recitals

Recital presented at the UNT College of Music Kenton Hall in partial fulfillment of the Doctor of Musical Arts (DMA) degree

A Novel Insight of Effects of a 3-Hz Binaural Beat on Sleep Stages During Sleep

The dichotic presentation of two almost equivalent pure tones with slightly different frequencies leads to virtual beat perception by the brain. In this phenomenon, the so-called binaural beat has a frequency equaling the difference of the frequencies of the two pure tones. The binaural beat can entrain neural activities to synchronize with the beat frequency and induce behavioral states related to the neural activities. This study aimed to investigate the effect of a 3-Hz binaural beat on sleep stages, which is considered a behavioral state. Twenty-four participants were allocated to experimental and control groups. The experimental period was three consecutive nights consisting of an adaptation night, a baseline night, and an experimental night. Participants in both groups underwent the same procedures, but only the experimental group was exposed to the 3-Hz binaural beat on the experimental night. The stimulus was initiated when the first epoch of the N2 sleep stage was detected and stopped when the first epoch of the N3 sleep stage detected. For the control group, a silent sham stimulus was used. However, the participants were blinded to their stimulus group. The results showed that the N3 duration of the experimental group was longer than that of the control group, and the N2 duration of the experimental group was shorter than that of the control group. Moreover, the N3 latency of the experimental group was shorter

A robust minimum variance beamforming approach for the removal of the eye-blink artifacts from EEGs

In this paper a novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on the robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, i.e., an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, namely the vector corresponding to the spatial distribution of the EB factor, is identified using a novel space-time-frequency-time/segment (STF-TS) model of EEGs, provided by a four-way parallel factor analysis (PARAFAC) approach. The results demonstrate that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements

Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal–spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT)

Fast STF Model and Applications on EEG Analysis

Searching for the tool that can efficiently summarize a multi-channel EEG signal is a challenging problem in EEG processing. In this paper, we propose the fast implementation of the 3-way parallel factor analysis (PARAFAC) called Fast STF model (fSTF model) which can simultaneously employ all the space, time, and frequency domains of a multi-channel EEG. The multi-channel EEG signal is first subdivided along space and time domains into the selected numbers of segments. By carefully selecting the number of segments according to the structure of the brain, signatures (features) extracted from the fSTF model are comparable with those from the conventional STF model while the time used in computation is reduced by more than 50%. Signatures obtained from the fSTF model are further summarized as a single number to indicate the quality of the multi-channel EEG signal. The simulation results illustrate the merits of the proposed model via the applications on eyeblink artifact-contaminated EEG decomposition and EEG quality assessment.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Oral session: Advances in Signal Processing for Brain Data Analysis and Feature Extraction (5 October 2009)