Working With Environmental Noise and Noise-Cancelation: A Workload Assessment With EEG and Subjective Measures

Pieper, Kerstin; Spang, Robert P.; Prietz, Pablo; Möller, Sebastian; Paajanen, Erkki; Vaalgamaa, Markus; Voigt-Antons, Jan‐Niklas

doi:10.3389/fnins.2021.771533

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…The noncasual bandpass filter in the time-domain was applied using Firwin's method. Firwin's bandpass method was used to filter out high-frequency noise from EEG electrical equipment and to exclude slow drifts in the signal [16]. The characteristics of the one-pass, zero-phase bandpass were a lower transition bandwidth (-6 dB cutoff frequency) and an upper transition bandwidth, depending on the frequency band's cutoff frequencies.…”

Section: Signal Preprocessingmentioning

confidence: 99%

Using Electroencephalographic Signal Processing and Machine Learning Binary Classification to diagnose Schizophrenia

Desai

2023

Preprint

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Electroencephalography (EEG) is an electrical activity measurement technique used to identify brain activity in Schizophrenic patients. Novel machine learning methods have emerged with useful applications for Schizophrenia classification. This research aims to compare the performance of several models post signal processing, such as Random Forest (RF), Support Vector Machine (SVM), Extra Trees (ET), and K-Nearest Neighbor (KNN), in the classification of healthy and Schizophrenic patients. The dataset used in this study contains 14 healthy and 14 Schizophrenic patients (n=28), with 17 channels and 2 reference electrodes designated to each patient, from the Nalecz Institute of Biocybernetics and Biomedical Engineering and the Institute of Psychiatry and Neurology in Warsaw, Poland. Signal processing feature extraction was performed using time-series or frequency-series Electroencephalographic data. The results suggest that Random Forest achieved the best performance metrics, achieving an accuracy, precision, recall, F1 score, and AUC of 92.4%, 95.5%, 95.5%, 0.939, and 0.932, respectively. These results propose that machine learning algorithms can be used to classify hospitalized patients who may have Schizophrenia, a useful supplement to additional clinical diagnosis performed by physicians.

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Section: Signal Preprocessingmentioning

confidence: 99%

Using Electroencephalographic Signal Processing and Machine Learning Binary Classification to diagnose Schizophrenia

Desai

2023

Preprint

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“…Therefore, many physiological indicators have been used to estimate MWL, such as electrocardiograms (ECG), eye movements, electroencephalography (EEG) measurements, respiration, and electromyography (EMG). Among these physiological indicators, EEG is widely used because MWL changes are closely linked to brain cortical activity and because it is non-smooth, noninvasive, and highly discriminative (Wilson et al, 1994;Dehais et al, 2020;Pieper et al, 2021;Liu et al, 2022;Yu et al, 2022). This is why EEG is also known as the gold standard.…”

Section: Introductionmentioning

confidence: 99%

EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation

Fan

Huang

et al. 2022

Front. Neurosci.

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The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation systems involving handcrafted EEG features are time-consuming and unsuitable to apply in real-time. The purpose of this study was to propose an end-to-end BCI framework for MWL estimation. First, a new automated data preprocessing method was proposed to remove the artifact without human interference. Then a new neural network structure named EEG-TNet was designed to extract both the temporal and frequency information from the original EEG. Furthermore, two types of experiments and ablation studies were performed to prove the effectiveness of this model. In the subject-dependent experiment, the estimation accuracy of dual-task estimation (No task vs. TASK) and triple-task estimation (Lo vs. Mi vs. Hi) reached 99.82 and 99.21%, respectively. In contrast, the accuracy of different tasks reached 82.78 and 66.83% in subject-independent experiments. Additionally, the ablation studies proved that preprocessing method and network structure had significant contributions to estimation MWL. The proposed method is convenient without any human intervention and outperforms other related studies, which becomes an effective way to reduce human factor risks.

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Generalized EEG Data Acquisition and Processing System

Carthen

Kamaruddin

et al. 2023

Advances in Intelligent Systems and Computing

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Working With Environmental Noise and Noise-Cancelation: A Workload Assessment With EEG and Subjective Measures

Cited by 7 publications

References 46 publications

Using Electroencephalographic Signal Processing and Machine Learning Binary Classification to diagnose Schizophrenia

Using Electroencephalographic Signal Processing and Machine Learning Binary Classification to diagnose Schizophrenia

EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation

Generalized EEG Data Acquisition and Processing System

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