Auditory Evoked Potential Response and Hearing Loss: A Review

Paulraj, Michael Gabriel; Subramaniam, Kamalraj; Yaccob, Sazali Bin; Adom, Abdul Hamid; Hema, C. R.

doi:10.2174/1874120701509010017

Cited by 48 publications

(32 citation statements)

References 46 publications

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“…EEG is extensively used for clinical applications such as epilepsy diagnostics (Noachtar and Rémi, 2009), sleep staging (Campbell, 2009), diagnosis of hearing loss (Paulraj et al, 2015), anesthesia monitoring (Marchant et al, 2014), and brain-computer interfaces (Shih et al, 2012). Moreover, EEG serves as a fundamental research tool for understanding human brain function (Lopes da Silva, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

Bleichner

Debener

2017

Front. Hum. Neurosci.

159

193

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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 positioning 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 enough 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 open issues that need to be solved on the way toward transparent EEG.

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Section: Introductionmentioning

confidence: 99%

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

Bleichner

Debener

2017

Front. Hum. Neurosci.

159

193

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“…ERPs also provide non-invasive neurophysiological measurements with high temporal resolution, allowing to assess dysfunctional brain dynamics, including cognitive processes that may not be apparent at the behavioral level (Woodman, 2010;Sanei and Chambers, 2013). Indeed, ERPs are commonly used clinically in neurophysiological diagnostic units to support the assessment of neuropsychiatric disorders [e.g., multiple sclerosis (Pokryszko-Dragan et al, 2016)] and sensory disorders [e.g., screening of neonates for hearing impairments (Paulraj et al, 2015)].…”

Section: Introductionmentioning

confidence: 99%

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

Dubreuil-Vall

Ruffini²,

Camprodon

2020

Front. Neurosci.

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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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“…It is known that P2 is related to discrimination skills, while N2 involves attention. 1,2,4 Thus, it is noted that the absence of P2 and N2 in some infants may be a maturational problem, but another hypothesis may be the use of a syllable as an acoustic stimulus. Perhaps such a stimulus is not interesting to evidence such components.…”

Section: Discussionmentioning

confidence: 99%

“…Auditory evoked potentials (AEPs) measure the electrical activity generated at several levels of the nervous system in response to acoustic stimuli. 1,2 Auditory evoked potentials are classified based on their latency into the following categories: brainstem auditory evoked potentials (BAEPs) have a latency of 0 to 10 ms, and consist of short latency potentials which travel from the auditory nerves to the brainstem; Middle-Latency Auditory Evoked Potentials (MLAEPs) have a latency of 10 to 80 ms; and Long Latency Auditory Evoked Potentials (LLAEPs) occur 80 to 600 ms after exposure to the stimulus, and are generated by the bioelectrical activity of thalamocortical neurons. 3 In neonates and infants, the study of AEPs contributes to the assessment of the sensitivity, maturation, and neuroplasticity of auditory pathways, consisting an important source of information on auditory processing and the potential need of sound amplification, facilitating the implementation of early intervention programs, if necessary.…”

Section: Introductionmentioning

confidence: 99%

Cortical Auditory Evoked Potentials in 2-Year-Old Subjects

Rechia

D'Agostini

Sousa

et al. 2019

Int Arch Otorhinolaryngol

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Introduction Cortical auditory evoked potentials (CAEPs) can be used to evaluate both peripheral and cortical components of auditory function, and contribute to the assessment of functional sensitivity and auditory thresholds, especially in neonates and infants. Auditory evoked potentials reflect auditory maturity and precede the acquisition of more complex auditory and cognitive skills, and are therefore crucial for speech and language development. Objective The aim of the present study was to determine the presence, latency and amplitude of CAEP components in response to verbal stimuli in children aged 2 years old. Methods The sample consisted of 19 subjects, 10 of whom were male while 9 were female. All of the participants were 24 months old at the time of assessment. Results A total of 17 of the participants displayed all components of the CAEP. Additionally, no significant differences were observed between genders or ears in the present sample. The presence of all components of the CAEP in subjects aged 2 years old confirms the existence of a critical period for the maturation of auditory pathways in the first 2 years of life. Conclusion In the present study, in addition to the P1/N1 components, it was possible to observe the presence of the CAEP P2/N2 components in individuals aged 24 months, confirming the existence of a critical period for the maturation of the auditory pathways in the first 2 years of life.

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Auditory Evoked Potential Response and Hearing Loss: A Review

Cited by 48 publications

References 46 publications

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

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

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

Cortical Auditory Evoked Potentials in 2-Year-Old Subjects

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