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BIOPAC Systems (MP150 Model) : 16 Channels Real-Time Recorder

For recording EEG, EMG, ECG, EOG, EGG, Blood Pressure, etc. Compatibility with MATLAB, Labview, VC++

 

EEG electrode caps (small, medium) 10/20 International System
Disposable EMG electrodes
Impedance meter and other accessories

 

AcqKnowledge 4.1, Data acquisition software

 

 

●○● Main Research Topics


 

Example of experiments on EEG study

 

Cognitive Science (decision making, working memory, perception, intelligence, emotion, behavioral response, neuro-based studies)

Physiological Signal Processing (biofeedback, brain-machine interface, EEG signal classification, ERP, ERD/ERS)

Fractal Analysis and Chaos Theory (fractal features, dynamical systems and chaos in EEG)

Speech Processing (speech enhancement, speech recognition, speech segmentation)

Image Processing (fractal dimension of texture image, object recognition, real-time image processing)

 

 

●○● Example of Research Interests

Differential Box-Counting Method for Estimation of Gray-Level Image Fractal Dimension

Fractal dimension (FD) of image studies originated with the principle found in the differential box-counting (DBC) method [1]. This study is to implement simple DBC on the basis of the concept of self-similarity found in gray images. The FD of a bounded set A in Euclidean n-space is given by Nr α (1/r)D where Nr is the least number of distinct copies of A in the scale r.

Estimated FDs by DBC method (Left) Texture image D04 (Right) Texture image D33

 

   

3-D space (Left) Texture image D04 (Right) Texture image D33

If we consider an image of size MxM pixels as a 3-D space with (x,y) representing 2-D and the third coordinate (z) representing the gray intensity of image, the (x,y) space is partitioned into grids of size sxs by the DBC method where 1 < s <= M/2 [3]. In this experiment, we set s = 2, 4, ..., M/2.

Comparison of results of FDs of Brodatz texture image [2]

Brodatz Texture Images Implemented Method Conventional Method [1]
FD MSE FD MSE
D03 2.5801 0.0349 2.60 0.032
D04 2.6467 0.0247 2.66 0.026
D05 2.3795 0.0305 2.45 0.032
D33 2.3474 0.0087 2.23 0.007
D84 2.6216 0.0435 2.60 0.029
D92 2.5217 0.0340 2.50 0.023
D68 2.5306 0.0221 2.52 0.024
D55 2.4670 0.0304 2.48 0.031

References
[1] N. Sarker and B.B. Chaudhuri, "An Effective Differential Box-Counting approach to Compute Fractal Dimension of Image", IEEE Trans. Syst. Man Cyb., vol.24, no.1, pp.115-120, 1994.
[2] Brodatz Texture Image Database, SAMP Lab, Ohio State University, Http://sampl.ece.ohio-state.edu/database.htm.
[3] J. Li, C. Sun, and Q. Du, "A New Box-Counting Method for Estimation of Image Fractal Dimension", IEEE Int. Conf. Img. Proc. (ICIP'06), pp.3029-3032, 2006.

 

Nondestructive Maturity Classification of Durian Based on Fractal Dimension Analysis

The objective of this research was to determine maturity levels of durian by using fractal analysis. Based on the computation of classical methods, it is difficult to apply theses methods to extract feature patterns of two different classes. Therefore, in this research, we used the automatic knocking machine [1] to knock the durian in which the knocked sound was analyzed in terms of fractal concepts, and the fractal dimension (FD) values would be presented as a feature. The fractal algorithm, namely, Higuchi’s method [2] was selected to evaluate FD of the knocked sound. To show the FD changes in waveform with respect to time, the time-dependent FD (TDFD) was proposed. Probability distribution of TDFDs indicates that the relationship of immaturity and maturity levels of durian has proven that FD-based features can be employed.

 

 

Classical methods (Left) Probability distribution function of two classes (Right) Corresponding normalized entropy

 

Comparison of average TDFD waveforms of maturity and immaturity (Monthong durian weight 3.0 – 3.5 kg.)

 

Attractors in phase space
 

The proposed method achieves the average accuracy rates of 89.94% and 88.20% for the "Monthong" durians weighting 3.5–4.0 kg and 4.0–4.5 kg, respectively. The experimental results also show that FD is capable of classifying maturity levels of durian effectively.

References
[1] T. Paritwanon, P. Somboonyod, W. Bundit, and M. Phothisonothai, "Non-Destructive Fruit Maturity Determination by using Knock Sound Processing" The 10th Annual Conf. TSAE (in Thai), pp.344-349, Nakhon Ratchasima, Thailand, April 2009.
[2] T. Higuchi, “Approach to An Irregular Time Series on the Basis of the Fractal Theory,” Physica D, vol.31, 277–83, 1988.

 

Thai Speech Processing Based on Fractal Theory

In the past, many methods have been developed to analyze Thai speech signals [1]–[3], e.g., energy, frequency, entropy, wavelet, etc. In this present study, for the first time, we try to investigate Thai speech signals based on the fractal concept in order to show whether a complexity of vowels and consonants can be distinguished. To determine the quantity’s complexity, fractal dimension (FD) value is proposed due to it begin one of the indicative parameters most widely used.


Example of Thai sentence "ฉุกเฉิน /Chook/Chan/" means "emergency" (click this to hear it)
(Upper) Waveform in time domain (Lower) Time-frequency domain

The method to estimate FD that we used in this study is the modified zero-crossing rate (MZCR) method [4]. The main principle of MZCR is the assumption that high complexity can be easily found by obtaining the highest rate of the zero-crossing point. It means that we can directly compute the complexity of speech signal on the basis of the zero-crossing rate function. The FD can be defined by: FD = 2 + m where m is a scaling parameter. In the MZCR method, there are three main steps to determine the scaling parameter of FD. This computation is repeated over all possible interval lengths (in practice, we suggest minimum length be 24-point and maximum length be 2n-1-point).


FD waveform in fractal domain estimated by the MZCR method

By applying the MZCR method with the windowing process, we can show the trajectory of  FD values from the original speech signal with respect to the number of windows (small segmentation).

   
Probability distribution of two separated syllables are that "/ฉุก/เฉิน/ /Chook/Chan/"
(Left) "ฉะ/Ch/" consonant is marked in blue, "อุก/ook/" vowel is marked in red
(Right) "ฉะ/Ch/" consonant is marked in blue, "เออ/aa/" vowel is marked in red, "น/n/" final consonant is marked in green

In Thai language, a syllable consists of a consonant, vowel, tone, and final consonant. This study shows that we can simply separate the  components of Thai syllables by using the FD values.

References
[1] N. Jittiwarangkul, et al., "Thai Syllable Segmentation for Connected Speech Based on Energy", IEEE APCCAS, pp.169-172, Nov. 1998.
[2] N. Satravaha, et al., "Tone Classification of Syllable-Segmented Thai Speech on Multilayer Perceptron", Proceedings of the 35th Southeastern Symposium on System Theory, pp.392- 396, Mar. 2003.
[3] K. Chamnongthai, at al., "Final Consonant Segmentation for Thai Syllable by using Vowel Characteristics and Wavelet Packet Transform", ECTI Trans. Comp & Info Theo. vol.1,no.1, pp.50-62, May 2005.
[4] M. Phothisonothai and M. Nakagawa, "A Complexity Measure Based on Modified Zero-Crossing Rate Function for Biomedical Signal Processing", The 13th Int. Conf. on Biomed. Eng. (ICBME2008), vol.23, pp. 240-244, Singapore 2008.

 

Algorithm Development for Measuring Complexity of Time-Series Data

 

 

A complexity measure is a mathematical tool for analyzing time-series data in many research fields. Various measures of complexity were developed to compare time series and distinguish whether input time-series data are regular, chaotic, or random behavior. In the field of physiological signal analysis, complexity of heart and brain data can distinguish emotion, imagination, movement, etc. This study proposes a simple technique to measure quantity’s complexity on the basis of the rate values of a zero-crossing point. The conventional method, namely, Higuchi’s algorithm, has been selected for comparison in this study. The obtained results show that this proposed method is able to measure the complexity of time-series data by estimating the Hurst exponent which presents as a negative value.

 

Electroencephalogram Signal Classification for Brain-Machine Interface

Brain-Machine Interface (BMI) enables the user to operate with devices through electroencephalogram (EEG) signals. Research and development on BMI technology has led to effective applications in the real world, improving quality of life and reducing social costs. Many BMI applications in the real world allow a user to operate, e.g., virtual keyboards, cursors, wheelchairs, speech synthesizers, and assistance appliances. It also gives the user access to Internet, characters classifier, computer games and brain-controlled robots. Moreover, we are also interested in researching rehabilitation and assistive technology using other biomedical signals (EKG, MEG, etc.), for examples.

 

EEG Signal Analysis Based on Fractal Concepts

The objective of this study is to analyze the spontaneous EEG signal corresponding to body parts movement imagery tasks in terms of fractal properties. Six algorithms of fractal dimension (FD) estimators: box-counting algorithm, Higuchi algorithm, variance fractal algorithm, detrended fluctuation analysis, power spectral density analysis, and critical exponent analysis are proposed in this experiment.

 

Speech Enhancement Using Intelligent Algorithms

   

Background noises interfere with communication devices such as mobile telephones, digital hearing aids, etc. Therefore noise reduction (NR) for limiting the effect of these noises is important. The study proposes a noise reduction method based on soft decision-making by the fuzzy inference system (FIS). The different characteristics of noises frequently occurring are used for creating the fuzzy decision rule base of the FIS. The FIS has two input parameters: the average energy and the difference of the average energy. The analysis of the FIS is done in the domain of the perceptual wavelet packet transform (PWPT) that is the human’s psychoacoustic model. The output of the FIS is used to modify the PWPT coefficients in such a way that it is more likely that the noise components are reduced while the speech signal is enhanced. The enhanced speech signal is the result of the inverse perceptual wavelet packet transform (IPWPT) of the modified coefficients.