Proceedings ArticleDOI
A new perspective of noise removal from EEG
Junhua Li,Chao Li,Nitish V. Thakor,Andrzej Cichocki,Anastasios Bezerianos +4 more
- pp 501-504
TLDR
Data portions that are contaminated by noise are entirely removed and then restored according to their relationships with the remaining signal to purify the signal through addition rather than deduction that is normally executed in conventional methods.Abstract:
Denoising, noise or interferences are removed from recorded signal to enhance the signal-to-noise ratio (SNR), is a crucial and ubiquitous step in the procedure of signal processing, especially for neurophysiological signal. This step facilitates following processing, such as feature extraction, classification, and data analyses. Conventional methods are based on the principle of separating noise components from the recorded signal and removing them, but these methods do not remove noise completely. In particular, conventional methods seems powerless to eliminate irregular and occasional noise bursts, which are caused by transient electrode contacting problem, head movements, or unpredictable factors. In this paper, we tackled the problem of noise removal from a new perspective, which is opposite to the conventional methods. Data portions that are contaminated by noise are entirely removed and then restored according to their relationships with the remaining signal. The rationale of this procedure is to purify the signal through addition rather than deduction that is normally executed in conventional methods. The results of both synthetic data and real EEG demonstrated that our idea is feasible and provides a new promising manner for noise removal.read more
Citations
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Journal ArticleDOI
A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals
TL;DR: A one-dimensional residual Convolutional Neural Networks (1D-ResCNN) model for raw waveform-based EEG denoising is proposed to solve the above problem and can yield cleaner waveforms and achieve significant improvement in SNR and RMSE.
Journal ArticleDOI
PhysioUnicaDB: a dataset of EEG and ECG simultaneously acquired
TL;DR: The dataset PhysioUnicaDB is presented; it consists of 22 acquisition from healthy subjects, in which the EEG and ECG signals are simultaneously acquired, and the data are not filtered, so leaving the data as raw as possible.
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Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity
TL;DR: This chapter focuses on the literature ascertaining macro-scale representations of cognitive states from the perspective of brain connectivity and gives an overview of achievements related to cognitive states to date, especially within the past ten years.
Proceedings ArticleDOI
Predicting Subjective Sleep Quality Using Recurrent Neural Networks
TL;DR: Insight is provided into how RNNs can be used to extract information from brain signals and how methods such as hierarchical clustering analysis can help neural networks predict subjective variables from polysomnography data.
Journal ArticleDOI
Cognitive State Analysis, Understanding, and Decoding from the Perspective of Brain Connectivity
TL;DR: In this paper , the authors provide an overview of achievements related to cognitive states to date, especially within the past 10 years, and summarize the progress in cognitive state investigation, including analysis, understanding, and decoding.
References
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