Electroencephalographic (EEG) recordings are often contaminated by artifacts, i.e., signals with noncerebral origin that might mimic some cognitive or pathologic activity, this way affecting the clinical interpretation of traces. Artifact rejection is, thus, a key analysis for both visual inspection and digital processing of EEG. Automatic artifact rejection is needed for effective real time inspection because manual rejection is time consuming. In this paper, a novel technique (Automatic Wavelet Independent Component Analysis, AWICA) for automatic EEG artifact removal is presented. Through AWICA we claim to improve the performance and fully automate the process of artifact removal from scalp EEG. AWICA is based on the joint use of the Wavelet Transform and of ICA: it consists of a two-step procedure relying on the concepts of kurtosis and Renyi's entropy. Both synthesized and real EEG data are processed by AWICA and the results achieved were compared to the ones obtained by applying to the same data the “wavelet enhanced” ICA method recently proposed by other authors. Simulations illustrate that AWICA compares favorably to the other technique. The method here proposed is shown to yield improved success in terms of suppression of artifact components while reducing the loss of residual informative data, since the components related to relevant EEG activity are mostly preserved.

Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA

Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GA-BAergic agents. Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures (Shannon WE (SWE), Tsallis WE (TWE) and Renyi WE (RWE)), Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures (Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)). Two EEG data sets from sevoflurane-induced and isoflu-rane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, phar-macokinetic / pharmacodynamic (PK/PD) modeling and prediction probability analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline vari-ability, higher coefficient of determination and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an ad-vantage in computation efficiency compared with MDFA. Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Significance: Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.

/pdf/eeg-entropy-measures-in-anesthesia-pr085q2ztp.pdf

EEG entropy measures in anesthesia

Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System

Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data. In this paper, the unsupervised wavelet transform (WT) decomposition technique was systematically evaluated for the effectiveness of OA removal for a single-channel EEG system. A set of seven raw EEG data set was analyzed. Two commonly used WT methods, Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), were applied. Four WT basis functions, namely, haar, coif3, sym3, and bior4.4, were considered for OA removal with universal threshold and statistical threshold (ST). To quantify OA removal efficacy from single-channel EEG, five performance metrics were utilized: correlation coefficients, mutual information, signal-to-artifact ratio, normalized mean square error, and time-frequency analysis. The temporal and spectral analysis shows that the optimal combination could be DWT with ST with coif3 or bior4.4 to remove OA among 16 combinations. This paper demonstrates that the WT can be an effective tool for unsupervised OA removal from single-channel EEG data for real-time applications.

Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data

Electroencephalographic (EEG)recordings are affected data-segment, butitleads toaconsiderable infor- employed inordertoinvestigate thebrainactivity inneu- mation loss. A verypowerful approach, butstill notcommon ropathological subjects. Unfortunately EEG areoften contam-inclinical practice, wasproposed inliterature someyears inated bytheartifacts, signals thathavenon-cerebral origin iccticaroposinlitratu omeyears andthatmight mimiccognitive or pathologic activity and agic onsinartact signals extraction, detection therefore distort theanalysis ofEEG.Inthis paper weproposeandcancellation. Thisapproach concentrates theartifactual amultiresolution analysis, based onEEGwavelet processing, to content oftheEEGdataset inafewsignals toberejected, so extract thecerebral EEG rhythms. We alsopresent amethod that wedonothavetocancel theentire affected datasegment basedonRenyi's entropy andkurtosis toautomatically identify(1),(2),(3),(4). theWavelet components affected byartifacts. Finally, wediscuss asthejoint useofwavelet analysis, kurtosis andRenyi's entropy Obviously, artifact rejection always involves a lossof allows foradeeper investigation ofthebrainactivity andwe information, eventhough small, anditcauses alittle BEG discuss thecapability ofthis technique tobecomeanefficient distortion. Inaddition, manyartifacts havefixed characteris- preprocessing step tooptimize artifact rejection fromEEG.This tics inthefrequency domainandtheydistort theEEGonly isthefirst technique thatexploits thepeculiarities ofEEG to inaspecific frequency range. Optimize EEG artifact detection. i pcfcfeunyrne

Carlo Morabito

Papers

Brain Activity Investigation byEEGProcessing: Wavelet Analysis, Kurtosis andRenyi's Entropy forArtifact Detection