In this project, we propose a CNN model to classify single-channel EEG for driver drowsiness detection. We use the Class Activation Map (CAM) method for visualization. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.

Toolbox for detecting alpha and sleep spindles in EEG

Sleep EEG Event Detector (SEED) model, a deep learning-based detector of sleep spindles and K-complexes

In this project, we propose a CNN-LSTM model to classify single-channel EEG for driver drowsiness detection. We designed a visualization technique by taking advantage of the hidden states output by the LSTM layer. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles, as evidence for the drowsy state.

Multichannel Sleep Spindle Detector for sleep EEG

Detection of K-complexes and Sleep Spindles (DETOKS) using Sparse Optimization for sleep EEG

A new take on EEG sleep spindles detection exploiting a generative model (dynamic bayesian network) to characterize reoccurring dynamical regimes of single-channel EEG.

The Rodent Sleep Spindle Detector app can identify sleep spindles within intracranial EEG (iEEG) recordings.

Jupyter notebook/script to extract sleep spindles from 30 min EEG excerpts from 8 subjects/patients. The notebook is part of a project to fit the Quadratic Parameter Sinusoid (QPS) model to 11-16Hz spindles detected from the EEG excerpts.

This repository contains all Python scripts and Jupyter Notebooks that were used in the final stages of my undergraduate thesis. To summarise, the thesis involved the analysis of Stage N2 portions of 8 hour EEG recordings across 15 patients in order to extract 'sleep spindles'. The 'Quadratic Parameter Sinusoid' or QPS (Palliyali et. al. 2015) was used as a way to extract 6 quadratic polynomial coefficients that served as statistical descriptors of features of the extracted spindles (and non-spindles) such as their amplitudes, envelope symmetry, frequency, phase and more. The way this was achieved was using non-linear least squares (NLLS) via the Levenberg-Marquadt Algorithm (LM) as a way to perform a best fit of the model to the raw captured spindle. The main goal of the thesis was to use these 6 parameters as learning features in a simple feed-forward neural network in order to classify whether or not an acquired raw portion of an EEG signal is a spindle or not. The conclusion to the study showed that while the QPS model was a great way to reconstruct spindles and extract valuable coefficient data, there is no guarantee the non-linear regression will work since parameter initialisation is highly dependent on whether or not it is known (for certain) if the acquired raw section of the EEG is a spindle or not.

Scripts and figures that included in the paper

A "Sleep Spindle" Detector that detects Sleep Spindles from raw EEGs files (the file format is .edf). To do this, the code uses 4 parameters: Absolute Sigma Power; Relative Sigma Power; Moving Correlation; Moving Root Mean Square.

Detect event in EEG data such as sleep spindles and blinks

Detect HFOs, spikes, and spindles in EEG data.

EEG Preprocessor for sleep spindle detection

Matlab automatic spindle detector pipeline for extracting sleep spindles epochs from infant sleep EEGs

Swin-Wavelet: End-to-end and flexible sleep spindles detection for EEG signals based on wavelet transform and Swin-Transformer

S5: Self-Supervised Learning Boosts Sleep Spindle Detection in Single-Channel EEG via Temporal Segmentation

The project consists of estimating the average of slow and fast spindles and locating them in inverse space.

EEG-Spindle-Active-Learning

Detect sleep spindles from EEG signals (single-lead EEG, no hypnogram required)

MEG_EEG spindle detection algorithms

Analysis of EEG signal. Signal filtering, searching for sleep spindles

This project focus on sleep spindles searching in filtered EEG signal.

Easy script designed to find sleep spindles in EEG signal (school project)

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Source code for analysis, classification, and detection of sleep spindles from raw EEG data.

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MATLAB pipeline for wavelet-based sleep spindle analysis (EEG).