BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers. CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

• MeSH
• Algorithms MeSH
• Electroencephalography * methods   MeSH
• Evoked Potentials physiology   MeSH
• Humans MeSH
• Area Under Curve MeSH
• Signal Processing, Computer-Assisted MeSH
• Machine Learning MeSH
• Wavelet Analysis * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.

• MeSH
• Algorithms MeSH
• Phonocardiography methods   MeSH
• Signal Processing, Computer-Assisted * MeSH
• Signal-To-Noise Ratio MeSH
• Wavelet Analysis * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.

• MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Schizophrenia * MeSH
• Support Vector Machine MeSH
• Wavelet Analysis MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications.

• MeSH
• Algorithms * MeSH
• Video Recording MeSH
• Benchmarking MeSH
• Databases, Factual MeSH
• Electroencephalography methods   statistics & numerical data   MeSH
• Emotions classification   physiology   MeSH
• Humans MeSH
• Mathematical Concepts MeSH
• Brain anatomy & histology   physiology   MeSH
• Brain Waves physiology   MeSH
• Neural Networks, Computer MeSH
• Machine Learning MeSH
• Photic Stimulation MeSH
• Computational Biology MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

• MeSH
• Mathematical Computing MeSH
• Mathematics MeSH
• Numerical Analysis, Computer-Assisted * MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Počítačová věda. Výpočetní technika. Informační technologie
• NML Fields
• přírodní vědy
• přírodní vědy

• MeSH
• Image Interpretation, Computer-Assisted MeSH

• Conspectus
• Lékařské vědy. Lékařství
• NML Fields
• lékařská informatika

• MeSH
• Mathematics MeSH

• Conspectus
• Přírodní vědy. Matematické vědy
• NML Fields
• přírodní vědy

• MeSH
• Biomedical Engineering MeSH
• Electroencephalography MeSH
• Epilepsy MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Programming Languages MeSH
• Software MeSH
• Seizures MeSH
• Check Tag
• Humans MeSH
• Publication type
• Comparative Study MeSH