An Unsupervised Method for Artefact Removal in EEG Signals

Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

Section: Introductionmentioning

confidence: 99%

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Arevalillo‐Herráez

Cobos

Roger

et al. 2019

“…The methods in the second class are based on BSS approaches, typically Independent Component Analysis (ICA) algorithms [ 38 , 39 , 40 , 41 ], which are widely applied in several adult studies [ 20 , 26 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. However, the aforementioned inhomogeneities of the neonatal EEG and the similar amplitude and frequency content of the true brain activity and cardiac interference may reduce the effectiveness of ICA algorithms in separating artefactual signal components from components containing brain signals.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection and Removal of Cardiac and Pulse Interferences from Neonatal EEG Signals

Tamburro

Croce

Zappasodi

et al. 2021

Electrical cardiac and pulsatile interference is very difficult to remove from electroencephalographic (EEG) signals, especially if recorded in neonates, for which a small number of EEG channels is used. Several methods were proposed, including Blind Source Separation (BSS) methods that required the use of artificial cardiac-related signals to improve the separation of artefactual components. To optimize the separation of cardiac-related artefactual components, we propose a method based on Independent Component Analysis (ICA) that exploits specific features of the real electrocardiographic (ECG) signals that were simultaneously recorded with the neonatal EEG. A total of forty EEG segments from 19-channel neonatal EEG recordings with and without seizures were used to test and validate the performance of our method. We observed a significant reduction in the number of independent components (ICs) containing cardiac-related interferences, with a consequent improvement in the automated classification of the separated ICs. The comparison with the expert labeling of the ICs separately containing electrical cardiac and pulsatile interference led to an accuracy = 0.99, a false omission rate = 0.01 and a sensitivity = 0.93, outperforming existing methods. Furthermore, we verified that true brain activity was preserved in neonatal EEG signals reconstructed after the removal of artefactual ICs, demonstrating the effectiveness of our method and its safe applicability in a clinical context.

“…Nonetheless, an inherent disadvantage of BSS algorithms is that for each processed trial, the order is not preserved, which limits its direct application in further classifier stages used in BCI, where the order of the input vectors must be conserved to avoid loss of the adjustment parameters for each new data entry. Some automated BSS approaches have been proposed to discern between sources of interest and artifacts, and thus minimize the aforementioned inconvenience making use of statistical concepts [19,20,21].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network

Ortiz-Echeverri

Salazar-Colores

Rodríguez‐Reséndiz

et al. 2019

Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices on the grounds of brain activity. The noninvasive and most viable way to obtain such information is by using electroencephalography (EEG). However, these signals have a low signal-to-noise ratio, as well as a low spatial resolution. This work proposes a new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the Continuous Wavelet Transform (CWT), and a classification stage using a Convolutional Neural Network (CNN) approach. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.66% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art.