C. R. Hema

Auditory evoked potential response and hearing loss: a review.

Hypoacusis is the most prevalent sensory disability in the world and consequently, it can lead to impede speech in human beings. One best approach to tackle this issue is to conduct early and effective hearing screening test using Electroencephalogram (EEG). EEG based hearing threshold level determination is most suitable for persons who lack verbal communication and behavioral response to sound stimulation. Auditory evoked potential (AEP) is a type of EEG signal emanated from the brain scalp by an acoustical stimulus. The goal of this review is to assess the current state of knowledge in estimating the hearing threshold levels based on AEP response. AEP response reflects the auditory ability level of an individual. An intelligent hearing perception level system enables to examine and determine the functional integrity of the auditory system. Systematic evaluation of EEG based hearing perception level system predicting the hearing loss in newborns, infants and multiple handicaps will be a priority of interest for future research.

Asynchronous Brain Machine Interface-Based Control of a Wheelchair

A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.

EEG based hearing perception level classification using fractal features

The primary focus of this study is to develop a hearing ability level assessment system using auditory evoked potential signals (AEP). AEP signal is a non-invasive tool that provides auditory pathway information content and its stimulated interactions with neurons. To record the hearing perception levels at different sound intensity levels, namely, 20 dB, 30 dB, 40 dB, 50 dB and 60 dB in both the ears of a normal subject, a simple AEP based hearing perception level protocol has been proposed. The detrended fluctuation analysis (DFA) has been used to estimate the fractal values of different hearing perception levels of the recorded AEP signals. The extracted fractal features were then associated to different hearing perception levels of a subject and neural network models were developed. The maximum classification accuracy of the developed neural network models for the left and right ears are observed as 78.57% and 80.71% respectively. From the classification accuracy, it has been inferred that the neural network models are able to discriminate the five distinct hearing perception levels of a normal hearing person.

Brain machine interface: Classification of mental tasks using short-time PCA and recurrent neural networks

Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication, using the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental task EEG signals from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Principal component analysis is used for extracting features from the EEG signals. The EEG signal is classified into two tasks. Ten such task combinations are studied. Average classification accuracies varied from 75.5% to 100% with a testing error tolerance of 0.05. The classification performance of the proposed algorithm is found to be better compared to our earlier work using AR model features.

Neural network based classification of human emotions using Electromyogram signals

Facial expression of emotion is of great interest to many researchers. Facial Electromyography (FEMG) is used for the identification of different facial expressions namely happy, sad, fear, neutral, surprise etc. In this paper, a simple algorithm to identify six emotions using the FEMG signals is proposed. FEMG signals are recorded from seven subjects. The six emotions are identified using bandpower features extracted from the raw FEMG signals and neural networks. In this study, two networks are used to identify the emotions. The network has an average classification accuracy of 94.32%.

EEG based hearing perception level estimation for normal hearing persons

Auditory evoked potential (AEP) is a type of EEG signal emanated from the scalp of the brain by an acoustical stimulus. AEP response reflects the auditory ability level of an individual. In this paper, AEP signals were recorded by stimulating repetive click-sound of 1000 Hz at different stimulus intensity levels of 25 dB, 40 dB, 50 dB and 70 dB. Spectral entropy features of four distinct bands were extracted from the recorded AEP signal. The extracted features were associated to the hearing perception level of an individual and a neural network models was developed. The maximum classification accuracy of the developed neural network model was observed as 91.4 per cent in discriminating the specified stimulus intensity levels. From the result, it is clear that a different auditory stimuli level reflects corresponding hearing perception level of a person. This study might lead to a real-time practical system for non-invasively estimating the hearing perceptional level of a person.

Brain Machine Interface for physically retarded people using colour visual tasks

A Brain Machine Interface is a communication system which connects the human brain activity to an external device bypassing the peripheral nervous system and muscular system. It provides a communication channel for the people who are suffering with neuromuscular disorders such as amyotrophic lateral sclerosis, brain stem stroke, quadriplegics and spinal cord injury. In this paper, a simple BMI system based on EEG signal emanated while visualizing of different colours has been proposed. The proposed BMI uses the color visual tasks and aims to provide a communication through brain activated control signal for a system from which the required task operation can be performed to accomplish the needs of the physically retarded community. The ability of an individual to control his EEG through the colour visualization enables him to control devices. The EEG signal is recorded from 10 voluntary healthy subjects using the noninvasive scalp electrodes placed over the frontal, parietal, motor cortex, temporal and occipital areas. The obtained EEG signals were segmented and then processed using an elliptic filter. Using spectral analysis, the alpha, beta and gamma band frequency spectrum features are obtained for each EEG signals. The extracted features are then associated to different control signals and a neural network model using back propagation algorithm has been developed. The proposed method can be used to translate the colour visualization signals into control signals and used to control the movement of a mobile robot. The performance of the proposed algorithm has an average classification accuracy of 95.2%.

Design of a Wireless Sensor Fusion System to Analyze Conditions Inside Bore Wells

Bore well rescue operations are always challenging, as rescuing the child trapped inside bore wells must be done in the quickest possible time. In our earlier work, we have designed a bore well rescue device providing vital information about the conditions inside the bore well. In this paper, a wireless sensor fusion system is developed to collect the vital parameters such as humidity and temperature from the bore well at different climatic conditions. The sensor system is designed to provide information on the humidity level and concentration of gases inside the bore well which can aid the rescues operation. Experiments have conducted in open bore wells to access and analyses the atmospheric conditions inside bore well in the Mettupayam and Pollachi region, Tamil Nadu, India. We validated the proposed system in eight real rescue incidents with the help of Tamil Nadu Fire and Rescue department, India to save the child from the bore well at different places in Tamil Nadu, India. From our rescue operations, we observed that the monitoring the vital parameters such as humidity, temperature, oxygen level, CO and other gaseous level from the bore well using our proposed wireless sensor fusion system helps the rescue and paramedical team to take necessary actions immediately for rescuing the child safely in short time.

Auditory evoked potential based detection of hearing loss: A prototype system

Hearing loss has been the most prevalent sensory disability throughout the world. Over 275 million people around the world are affected by various hearing related problems. A conventional hearing screening tests applicability is limited as it requires a feedback response from the subject under test. To overcome such problems, the primary focus of this study is to develop an intelligent hearing ability level assessment system using auditory evoked potential signals (AEP). AEP signal is an electrical potential signal elicited from the brain while an auditory stimulus is presented in a time-locked manner. The AEP responses of normal hearing and abnormal hearing subjects were administered to fixed acoustic stimulus intensity in order to detect the hearing threshold level. The detrended fluctuation analysis (DFA) has been used to estimate the fractal values of the normal and abnormal hearing subjects. The extracted fractal features were then associated to hearing threshold level of the subjects. Feed-forward and feedback neural networks are employed to distinguish normal and abnormal hearing subjects. The classification performance of the proposed intelligent hearing ability level assessment system is in the range of 85-90%. This study indicates that mean fractal values of the abnormal hearing subjects are relatively higher while compared with the mean fractal values of the normal hearing subjects.

Improving classification of EEG signals for a four-state brain machine interface

Neural network classifiers are one among the popular modes in the design of classifiers for electroencephalograph based brain machine interfaces. This study presents algorithms to improve the classification performance of motor imagery for a four state brain machine interface. Dynamic neural network models with band power and Parseval energy density features are proposed to improve the classification of task signals. Motor imagery signals recorded noninvasively at the sensorimotor cortex region using two bipolar electrodes are used in the study. The performances of the proposed algorithms are compared with a static neural classifier. Average classification performance of 97.7% was achievable. Experiment results show that the distributed time delay neural network model out performs the layered recurrent and feed forward neural classifiers.