Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel.

Electroencephalogram (EEG) is often corrupted with artifacts originating from sources like the eyes and muscles. Hybrid artifact removal methods often require human intervention for the adjustment of different parameters. We propose a robust method that can automatically detect and remove eye-blink and muscular artifacts from EEG using k-nearest neighbor (kNN) classifier and a long short-term memory (LSTM) network. Our method adopts a sliding window of 0.5 s to detect and remove the artifacts from EEG. Features, such as the variance, peak-peak amplitude, and average rectified value are calculated for each EEG segment to identify corrupted segments using the kNN classifier. The kNN classifier detects the presence of artifacts, after which, the corresponding EEG window is forwarded to the LSTM network for artifact removal. The LSTM network is trained with the corrupted segments of 0.5 s as input and clean segments of 0.5 s as output. Our method achieved an accuracy of 97.4% in identifying corrupted EEG segments and an average correlation coefficient, structural similarity, signal to artifact ratio, and normalized mean squared error of 0.69, 0.76, 1.52 dB, and 0.0013, respectively, in cleaning the EEG. Our results outperformed other hybrid methods reported in the literature based on combination of ensemble empirical mode decomposition and canonical correlation analysis, combination of independent component analysis and wavelet decomposition, and tensor decomposition. The mean absolute error of our method is also better in comparison to other methods. Our method can be applied to single and multiple channels and does not require any tuning of parameters.

This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.

Emotion plays a vital role in understanding the affective state of mind of an individual. In recent years, emotion classification using electroencephalogram (EEG) has emerged as a key element of affective computing. Many researchers have prepared datasets, such as DEAP and SEED, containing EEG signals captured by the elicitation of emotion using audio–visual stimuli, and many studies have been conducted to classify emotions using these datasets. However, baseline power removal is still considered one of the trivial aspects of preprocessing in feature extraction. The most common technique that prevails is subtracting the baseline power from the trial EEG power. In this paper, a novel method called InvBase method is proposed for removing baseline power before extracting features that remain invariant irrespective of the subject. The features extracted from the baseline removed EEG data are then used for classification of two classes of emotion, i.e., valence and arousal. The proposed scheme is compared with subtractive and no-baseline-correction methods. In terms of classification accuracy, it outperforms the existing state-of-art methods in both valence and arousal classification. The InvBase method plus multilayer perceptron shows an improvement of 29% over the no-baseline-correction method and 15% over the subtractive method.

Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain–computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.