SleepEEGpy: a Python-based software integration package to organize preprocessing, analysis, and visualization of sleep EEG data

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 192, P. 110232 - 110232

Published: April 26, 2025

Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is growing interest advanced EEG analysis that requires extensive preprocessing improve the signal-to-noise ratio specialized algorithms. While many software packages exist, has unique needs (e.g., specific artifacts, event detection). Currently, investigators use different libraries for tasks a 'fragmented' configuration inefficient, prone errors, learning of multiple environments. This complexity creates barrier beginners. Here, we present SleepEEGpy, an open-source Python package simplifies analysis. SleepEEGpy builds on MNE-Python, PyPREP, YASA, SpecParam offer all-in-one, beginner-friendly comprehensive research, including (i) cleaning, (ii) independent component analysis, (iii) detection, (iv) spectral feature visualization tools. A dedicated dashboard provides overview evaluate data preprocessing, serving as initial step prior detailed We demonstrate SleepEEGpy's functionalities using overnight high-density from healthy participants, revealing characteristic signatures typical each vigilance state: alpha oscillations wakefulness, spindles slow waves NREM sleep, theta REM sleep. hope this will be adopted further developed by community, constitute useful entry point tool beginners research.

Language: Английский

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Synergistic integration of brain networks and time-frequency multi-view feature for sleep stage classification

Health Information Science and Systems, Journal Year: 2025, Volume and Issue: 13(1)

Published: Jan. 10, 2025

Language: Английский

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Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning

Sensors, Journal Year: 2024, Volume and Issue: 24(21), P. 6965 - 6965

Published: Oct. 30, 2024

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether poor accuracy in field is a result design temporal stimulation (block versus rapid event) or inherent complexity electroencephalogram (EEG) signals. Decoding perceptive signal responses subjects become increasingly complex due high noise levels nature brain activities. EEG have resolution non-stationary signals, i.e., their mean variance vary overtime. This study aims develop deep learning model for decoding subjects' rapid-event visual stimuli highlights major factors that contribute low classification task.The proposed multi-class, multi-channel integrates feature fusion handle complex, applied largest publicly available dataset consisting 40 object classes, with 1000 images each class. Contemporary state-of-the-art studies area investigating large number classes achieved maximum 17.6%. In contrast, our approach, which Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves 33.17% classes. These results demonstrate potential advancing offering future applications machine models.

Language: Английский

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Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis

Sensors, Journal Year: 2024, Volume and Issue: 24(16), P. 5265 - 5265

Published: Aug. 14, 2024

Sleep is a vital physiological process for human health, and accurately detecting various sleep states crucial diagnosing disorders. This study presents novel algorithm identifying stages using EEG signals, which more efficient accurate than the state-of-the-art methods. The key innovation lies in employing piecewise linear data reduction technique called Halfwave method time domain. simplifies signals into form with reduced complexity while preserving stage characteristics. Then, features vector six statistical built parameters obtained from function. We used MIT-BIH Polysomnographic Database to test our proposed method, includes 80 h of long different biomedical main classes. classifiers found that K-Nearest Neighbor classifier performs better method. According experimental findings, average sensitivity, specificity, accuracy on considering eight records estimated as 94.82%, 96.65%, 95.73%, respectively. Furthermore, shows promise its computational efficiency, making it suitable real-time applications such monitoring devices. Its robust performance across classes suggests potential widespread clinical adoption, significant advances knowledge, detection, management problems.

Language: Английский

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Emotion Detection using EEG Signals in Image and Video Processing: A Survey

International Journal For Multidisciplinary Research, Journal Year: 2024, Volume and Issue: 6(4)

Published: Aug. 31, 2024

Brain serves as the body's processing of knowledge and management centre. The central nervous system directly produces ElectroEncephaloGram (EEG) physiological signals, which are strongly associated with human emotions. In upcoming years, there will be an increase in interest for identifying emotion using brain waves by EEG (ElectroEncephaloGraphy) signals. It takes efficient effective signal feature extraction techniques detection emotions from biological Current approaches gather valuable information a fixed number ElectroEncephaloGraphy channels utilizing variety methodologies. This work analyses different difficulties problems signals identification provides comprehensive summary several contemporary approaches. Pre-processing, extraction, categorization first steps process recognizing main goal this survey is to sought enhance signal-based ability comparing all novel adaptive channel selection technique that recognize distinct changes activities varies between individuals emotional states.

Language: Английский

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Time-frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualization technique

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125490 - 125490

Published: Oct. 1, 2024

Language: Английский

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Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 183, P. 109305 - 109305

Published: Nov. 1, 2024

Language: Английский

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Wafformer: Wave-Frequency Cross-Prediction and Alignment for Knowledge-Enhanced Sleep Representation

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Journal Year: 2024, Volume and Issue: unknown, P. 2673 - 2679

Published: Dec. 3, 2024

Language: Английский

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