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An Introduction To The Event Related Potential Technique

Introduction: Past, Present and Potential Recording Evoked Potentials: Means to an End Frequency-Domain: Music of the Hemispheres Finding Sources: Forwards and Backwards Sounds to Charm the Brain Interpreting the Waveforms: Time and Uncertainty Electrocochleography: From Song to Synapse Auditory Brainstem Responses: Peaks Along the Way Middle Latency Responses - The Brain and the Brawn Auditory Steady State and Following Responses: Dancing to the Rhythms Late Auditory Evoked Potentials: Changing the Things Which Are Endogenous Auditory Evoked Potentials: Attention Must Be Paid Infant Hearing Assessment: Opening Ears Neurotology and Neurology: From Cochlea to Cortex Auditory Neuropathy: When Time is Broke Cochlear Implants: Body Electric Concluding Comments: Beginning to Live

Human Auditory Evoked Potentials

Brain computer interfacing: Applications and challenges

Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by distributing electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive and near invisible data acquisition with minimum disturbance of a user’s daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify and open issues that need to be solved on the way towards transparent EEG.

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Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG.

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis remains essentially clinical, based on history and exam, with no available biomarkers. In this paper, we describe a convolutional neural network (CNN) with a four-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG time-frequency decompositions (spectrograms) of electroencephalography data (EEG), particularly of event-related potentials (ERP) from ADHD patients (n = 20) and healthy controls (n = 20) collected during the Flanker Task, with 2800 samples for each group. We treat the data as in audio or image classification approaches, where deep networks have proven successful by exploiting invariances and compositional features in the data. The model reaches a classification accuracy of 88% ± 1.12%, outperforming the Recurrent Neural Network and the Shallow Neural Network used for comparison, and with the key advantage, compared with other machine learning approaches, of avoiding the need for manual selection of EEG spectral or channel features. The event-related spectrograms also provide greater accuracy compared to resting state EEG spectrograms. Finally, through the use of feature visualization techniques such as DeepDream, we show that the main features exciting the CNN nodes are a decreased power in the alpha band and an increased power in the delta-theta band around 100 ms for ADHD patients compared to healthy controls, suggestive of attentional and inhibition deficits, which have been previously suggested as pathophyisiological signatures of ADHD. While confirmation with larger clinical samples is necessary, these results suggest that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, highlighting the potential of this methodology to develop biomarkers of practical clinical utility.

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Deep Learning Convolutional Neural Networks Discriminate Adult ADHD From Healthy Individuals on the Basis of Event-Related Spectral EEG

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.

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Auditory evoked potential response and hearing loss: a review.

An auditory loss is one of the most common disabilities present in newborns and infants in the world. A conventional hearing screening test's 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 a non-invasive tool that can reflect the stimulated interactions with neurons along the stations of the auditory pathway. The AEP responses of fourteen normal hearing subjects to auditory stimuli (20 dB, 30 dB, 40 dB, 50 dB and 60 dB) were derived from electroencephalogram (EEG) recordings. Higuchi's fractal method is applied to extract the fractal features from the recorded AEP signals. The extracted fractal features were then associated to different hearing perception levels of the subjects. Feed-forward and feedback neural networks are employed to distinguish the different hearing perception levels. The performance of the proposed intelligent hearing ability level assessment found to exceed 85% accuracy. This study indicates that AEP responses to the auditory stimuli to the normal hearing persons can predict the higher order auditory stimuli followed by the lower order auditory stimuli and consequently the state of auditory development of subjects

A machine learning approach for distinguishing hearing perception level using auditory evoked potentials

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 test's 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.

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

In this paper, proposed a method to classify EEG time series signals recorded from left and right ears by presenting an acoustical stimulus in a time locked manner. Fractal dimensional features were extracted in order to measure the complexity of temporal dynamics and response onset of auditory evoked potentials of normal hearing and abnormal hearing persons. This study identified a significant potential difference between fractal dimensional values of the normal hearing and abnormal hearing person. The extracted fractal features were then associated to the hearing threshold perception and a neural network model for left and right ears were developed. The classification results in discriminating the left and right ear of normal and abnormal person was reported as 90% and 95% with specificity of 90%, sensitivity of 100%. Since the results were promising, it can be safely adopted in screening the hearing threshold level of a person in clinics.

EEG based hearing threshold classification using fractal feature and neural network

Electroencephalogram (EEG) is used to measure the bioelectric potential on the brain scalp. The recorded EEG signal can have different types of artifacts and the interpretation of a noisy EEG signal is difficult. In this research work, a simple method is proposed to minimize the artifacts present in the EEG signals recorded while perceiving a pure tone. The recorded EEG signal can contain artifacts, such as movement artifacts, muscle contraction artifacts and saturation artifacts. In the proposed method, fractal dimension based features with different interval length and time-domain based energy features were extracted from the EEG signals with and without simulated noise. Using the extracted features, neural network models were developed to classify the EEG signal as a normal or a noisy signal. Further, the performance of the model is also evaluated in terms of classification rate. From the results, it is observed that the neural network model developed with the combined fractal dimension features of interval length 2,3,4,5 and 6 with frame size 128 has the highest classification accuracy of 95.5%.

Sazali Bin Yaccob

Papers

Auditory evoked potential response and hearing loss: a review.

A machine learning approach for distinguishing hearing perception level using auditory evoked potentials

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

EEG based hearing threshold classification using fractal feature and neural network

Fractal feature based detection of muscular and ocular artifacts in EEG signals