One typical step in many studies is feature extraction, however, there are not many tools focused on that aspect. In this paper, eeglib: a Python library for EEG feature extraction is presented. It includes the most popular algorithms when working with EEG and can be easily combined with ...
In response, we have developed PyEEG, a Python module for EEG feature extraction, and have tested it in our previous epileptic EEG research [3, 8, 11]. Compared to other popular programming languages in scientific computing such as C++ or MATLAB, Python is an open source scripting languag...
In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. 1. Introduction Computer-aided diagnosis based on EEG has become possible in the last decade for several neurological diseases such as Alzheimer′s disease [1, 2] and epilepsy [3, 4]. Implemented ...
不同的刺激在EEG信号中引发不同的反应。将使用不同类型的视频刺激及其相应的情绪效果，这是由EEG信号确...
In EEGPT, a mask-based dual self-supervised learning method for efficient feature extraction is designed. Compared to other mask-based self-supervised learning methods, it adds spatio-temporal representation alignment, constructing a self-supervised task on EEG representations with high SNR and rich ...
ly plausible and interpretable biomarkers of neurological and psychiatric disorders. They can be easily extended and adapted to specific study designs, as the modular configuration of the code allows for substituting, removing, or adding specific steps of the preprocessing and feature extraction....
Example code and a deployable notebook can be found in the GitHub repository. Source data used in all figures are provided with this paper. Source data are provided with this paper. Feature extraction and selection Temporal and spectral features were extracted in Python 3.8 from the segmented Ex...
03_dnn_feature_maps_extraction Added data collection code Mar 14, 2024 04_synthesizing_eeg_data Added data collection code Mar 14, 2024 05_synthetic_data_analyses Added data collection code Mar 14, 2024 06_plotting Added data collection code ...
Unique in its use of depth-wise convolutions and separable convolutional layers, EEGNet allows for efficient and expressive feature extraction. Both InceptionNetwork and EEGNet architecture underwent modifications to create various ensemble models (these changes can be found in the supplied code). These...
This classification system was implemented in Python without any special hardware optimization, utilizing multiple GPU and CPU devices. It integrates with Googles TensorFlow and Cuda, which crucially speed learning and testing. The aim of this section is to give some results and show the effectiveness...