International Conference on Bioinformatics and Biomedical Engineering 

About: International Conference on Bioinformatics and Biomedical Engineering is an academic conference. The conference publishes majorly in the area(s): Wastewater & Adsorption. Over the lifetime, 7053 publications have been published by the conference receiving 18048 citations.

Imagined Speech Classification with EEG Signals for Silent Communication: A Preliminary Investigation into Synthetic Telepathy

Abstract: The objective of this work is to explore the potential use of electroencephalography (EEG) as a means for silent communication by way of decoding imagined speech from measured electrical brain waves. EEG signals were recorded at University of California, Irvine (UCI) from 7 volunteer subjects imagining two syllables, /ba/ and /ku/, without speaking or performing any overt actions. Our goal is to classify these imagined syllables and based on the resulting accuracy assess the feasibility of this task. In this research, the EEG data are preprocessed to reduce the effects of artifacts and noise, and autoregressive (AR) coefficients are extracted as features for imagined syllable classification using a k-Nearest Neighbor classifier. Initial results suggest that it is possible to identify imagined speech.

Development of a Fall Detecting System for the Elderly Residents

Abstract: Accidental falls are common causes of serious injury and health threats in the elder population. To deliver adequate medical support, the robust and immediate falls detection is important. Since the fall detection in the elderly remains a major challenge in the public health domain, effective fall-detection will provide urgent support and dramatically reduce the cost of medical care. In this work, we propose a fall-detecting system placing an accelerometer on the head level and using an algorithm to distinguish between falls and daily activities. The experimental results have demonstrated the proposed scheme with high reliability and sensitivity on fall detection. The system is not only cost effectively but also potable. It fulfills the requirements of fall detection.

Identification of Individual Walking Patterns Using Gait Acceleration

Abstract: This paper presents an improved approach on identifying users based on three-dimensional gait acceleration signal characteristics produced by walking. When the user carries the wearable gait acceleration acquiring system, acceleration signals are registered by the accelerometer. Through dividing the signals into gait cycles, gait feature code which represents the walking pattern of the user can be extracted. Recognition is based on the general idea of template matching. We use dynamic time warping (DTW) algorithm for matching so that non-linear time normalization could be used to dispose the problems resulted from naturally occurring changes in walking speed. Experiments were performed on 35 healthy subjects walking on their normal speed; Equal Error Rate of 6.7% was achieved. Our preliminary experiments confirm the possibility of recognizing users based on their gait acceleration.

Photocatalytic Ozonation of Dimethyl Phthalate over TiO2 Prepared by a Hydrothermal Method

Abstract: TiO2 catalyst was prepared by two kinds of different methods. The photocatalytic activity of TiO2 prepared by the hydrothermal method is 2.5 times higher than that by sol-gel in degradation dimethyl phthalate (DMP). The combined photocatalysis with UV irradiation and ozonation (TiO2/UV/O3) process considerably improved mineralization and degradation of dibutyl phthalate compared to combined photocatalysis with UV irradiation (TiO2/UV) process, combined ozonation with UV irradiation (UV/O3) process and ozonation alone (O3) process. DMP can be quickly mineralized in TiO2/UV/O3, its mineralization process followed Langmuir-Hinshelwood model. Its rate constant k is 0.42 mg/ (L•min) and Langmuir adsorption coefficient K is 0.0417 L/mg.

Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier

Abstract: An accurate diagnosis is important for the medical treatment of patients suffered from brain disease Nuclear magnetic resonance images are commonly used by technicians to assist the pre-clinical diagnosis, rating them by visual evaluations The classification of NMR images of normal and pathological brains poses a challenge from technological point of view, since NMR imaging generates a large information set that reflects the conditions of the brain In this work, we present a computer assisted diagnosis method based on a wavelet-entropy (In this paper 2D-discrete wavelet transform has been used, in that it can extract more information) of the feature space approach and a Naive Bayes classifier classification method for improving the brain diagnosis accuracy by means of NMR images The most relevant image feature is selected as the wavelet entropy, which is used to train a Naive Bayes classifier The results over 64 images show that the sensitivity of the classifier is as high as 9450%, the specificity 9170%, the overall accuracy 9260% It is easily observed from the data that the proposed classifier can detect abnormal brains from normal controls within excellent performance, which is competitive with latest existing methods