From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

Elgendi, Mohamed; Dauwels, Justin; Rebsamen, Brice; Shukla, Rohit; Putra, Yosmar; Gámez, Jorge; Niu, Zeping; Ho, Bangying; Prasad, Niteesh; Aggarwal, Dhruv; Nair, Amrish; Mishuhina, Vasilisa; Vialatte, François; Constable, Martin; Cichocki, Andrzej; Latchoumane, Charles; Jeong, Jaesung; Thalmann, Daniel; Magnenat‐Thalmann, Nadia

doi:10.1007/978-1-4614-8151-5_4

Cited by 6 publications

(2 citation statements)

References 86 publications

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“…Regression neural networks are used to extract characteristics from the EEG, and then 10 threshold mechanisms are utilised to classify the data in [12]. Logistic regression makes use of gradient descent and optimization approaches, and the sigmoid function increases accuracy in identifying the presence of malignant or benign tissue for cancer investigation [13].…”

Section: Ml-dl Classification Methods For Eegmentioning

confidence: 99%

Classification of EEG signals using machine learning and deep learning techniques

Prasath

Lokesh

Krishnakumar³

et al. 2022

ijhs

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Electroencephalogram (EEG) signals reveal electrical activity of brain in a person. Brain cells interact by impulses even during sleep. Any disruptions to these impulses induce problems in the individual. Hence clinicians analyze electrical activity of the brain by EEG readings to comprehend the disruptions in the impulses. EEG and its sub bands depict electrical pattern of human brain. EEG data comprises transient components, spikes, other sorts of artifacts due to eye blinking, movement of the individual, anxiousness etc. during EEG collection. Wavelet transformations are effective mathematical technique for sampling approximation to produce clear EEG. It also assists in filtering, sampling, interpolation, noise reduction, signal approximation and signal augmentation, feature extraction. In this study, a survey is done on EEG motor imagery signals and the various classifiers to assess them machine learning methods for EEG signal categorization are studied. Conventional SVM and logistic regression methods combined with basic 2-layer Neural Network (NN) are constructed using python in keras to assess the performances.

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Section: Ml-dl Classification Methods For Eegmentioning

confidence: 99%

Classification of EEG signals using machine learning and deep learning techniques

Prasath

Lokesh

Krishnakumar³

et al. 2022

ijhs

View full text Add to dashboard Cite

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“…Mild cognitive impairment and mild Alzheimer are addressed using synchrony measures. Immersive multisided neural feedback obtained from visual and auditory feedbacks of brain waves are used for detecting Alzheimer disease in Dauwels et al 10 This method gives a healing effect and helps in bringing out novel diagnostic tools.…”

Section: Related Workmentioning

confidence: 99%

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

Nagabushanam

George

Dolly

et al. 2020

Circuit Theory & Apps

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Electroencephalogram (EEG) and its sub-bands represent electrical pattern of human brain. EEG signal contains transient components, spikes, and different types of artifacts due to eye blinking, movement of the person, anxiety, and so forth, during EEG capture. Wavelet transforms are powerful mathematical tool for sampling approximation to get clean EEG. It also helps in filtering, sampling, interpolation, noise reduction, signal approximation and signal enhancement, and feature extraction. In this paper, we have analyzed artifact cleaning via PSD graphs and statistical features extracted from motor imagery EEG-like standard deviation variance. For this, we considered 19 channels EEG signal and applied orthogonal Daubechies wavelet, bi-orthogonal rbio wavelet and Coifman wavelets to check the better performance of different wavelets. Coifman wavelet uses both scaling function and vanishing moments for sampling approximation and hence give smooth sampling compared to rbio and Daubechies wavelet transforms. Coif is a compactly supported wavelet system which also helps in smooth sampling approximations than other wavelets in the state of arts. The detailed coefficients and approximate coefficients can be further used for extracting features from EEG and classification purposes. Artifacts cleaning is thus observed better in coif wavelet analysis compared to other wavelets from the power distributions as power spectral density (PSD) graphs, standard deviation and variance obtained. Matlab R2013b is used for filtering and sampling EEG. Python 2.7 is used for statistical features extraction.

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Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions

et al. 2014

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The decoding of conscious experience, based on non-invasive measurements, has become feasible by tailoring machine learning techniques to analyse neuroimaging data. Recently, functional connectivity graphs (FCGs) have entered into the picture. In the related decoding scheme, FCGs are treated as unstructured data and, hence, their inherent format is overlooked. To alleviate this, tensor subspace analysis (TSA) is incorporated for the parsimonious representation of connectivity data. In addition to the particular methodological innovation, this work also makes a contribution at a conceptual level by encoding in FCGs cross-frequency coupling apart from the conventional frequency-specific interactions. Working memory related tasks, supported by networks oscillating at different frequencies, are good candidates for assessing the novel approach. We employed surface EEG recordings when the subjects were repeatedly performing a mental arithmetic task of five cognitive workload levels. For each trial, an FCG was constructed based on phase interactions within and between Frontal (θ) and Parieto-Occipital (α2) neural activities, which are considered to reflect the function of two distinct working memory subsystems. Based on the TSA representation, a remarkably high correct-recognition-rate (96%) of the task difficulties was achieved using a standard classifier. The overall scheme is computational efficient and therefore potentially useful for real-time and personalized applications.

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From Auditory and Visual to Immersive Neurofeedback: Application to Diagnosis of Alzheimer’s Disease

Cited by 6 publications

References 86 publications

Classification of EEG signals using machine learning and deep learning techniques

Classification of EEG signals using machine learning and deep learning techniques

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions

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