EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan E.

doi:10.3390/brainsci8110199

Cited by 21 publications

(9 citation statements)

References 84 publications

(85 reference statements)

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“…These signals can be recorded using different methods. These records, called electroencephalogram (EEG), contain a lot of information related to the work of the brain and other organs of the body [1,2,3]. This information also allows us to obtain information about both cognitive and sensory stimulation [1].…”

Section: Introductionmentioning

confidence: 99%

Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

2019

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The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.

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Section: Introductionmentioning

confidence: 99%

Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

2019

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“…The fast Fourier transform (FFT) decomposes time-domain data into frequency-domain data by determining a series of sine and cosine functions of different frequencies which may be added to obtain the time-domain data. [16]. The FFT can be used to determine the power spectral density, a measure of the strength of an EEG signal at a given frequency.…”

Section: Electroencephalography Fundamentalsmentioning

confidence: 99%

Examining the Use of Electroencephalography for the Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment

Abdallah

2021

URNCST Journal

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Introduction: Alzheimer's disease is a type of dementia characterized by a buildup of ꞵ-amyloid plaques and neurofibrillary tangles. Prior to the development of Alzheimer's disease, patients may experience mild cognitive impairment, characterized by a decline in cognitive abilities while maintaining independent function. Electroencephalography has shown promise as a clinical predictor of mild cognitive impairment. The purpose of this study is to review the existing literature on clinical biomarkers using resting-state electroencephalography or event-related potentials to differentiate Alzheimer's disease or mild cognitive impairment from normal aging. Methods: A search of primary research articles was conducted in PubMed. Selected articles examined mild cognitive impairment and Alzheimer's disease utilising electroencephalography, event-related potential data, and resting-state data. Reviews, conference abstracts, and studies without human controls were excluded. Results: Our search identified 100 and 125 records on resting-state and event-related potential data, respectively. The most common findings from resting-state studies included a reduction in alpha power, an increase in delta and theta power, a reduction in signal complexity, and differences in functional connectivity. The most common findings from event-related potential studies included reduction in P3 wave amplitude, as well as latency in both P3 and N2 waves. Discussion: Resting-state and event-related potential electroencephalography studies indicate distinct changes in oscillatory brain activity and waveform shape which indicate distinct differences in MCI or AD compared to HC which may be clinically relevant. Conclusion:There is evidence to support the use of certain electroencephalographic biomarkers for the diagnosis of Alzheimer's disease or mild cognitive impairment. Future research should seek to examine how best to apply these findings in a clinical setting.

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“…This type of neuroimaging has been used as a detection method for neurodegenerative diseases. For example, analyzing the sharpness of the brain signals is commonly used to detect epilepsy [13]. Thus, EEG studies can show how the activity in an ALS brain is changing or degenerating, offering specialists further comprehension of a disease whose evolutive nature is still unknown.…”

Section: Introductionmentioning

confidence: 99%

Functional Connectivity and Frequency Power Alterations during P300 Task as a Result of Amyotrophic Lateral Sclerosis

Perez-Ortiz

Gordillo

Mendoza-Montoya

et al. 2021

Sensors

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Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p < 0.05) were: an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The evolution observed over months of an advanced-state patient backs up these findings. These results were used to compute connectivity- and power-based features to discriminate between ALS and Control groups using Support Vector Machine (SVM). Cross-validation achieved a 100% in specificity and 75% in sensitivity, with an overall 89% success.

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EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

Cited by 21 publications

References 84 publications

Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

Examining the Use of Electroencephalography for the Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment

Functional Connectivity and Frequency Power Alterations during P300 Task as a Result of Amyotrophic Lateral Sclerosis

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