关于Python的面试题

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Python - 100天从新手到大师

Python面试宝典2020版

Python面试通关宝典，秋招、春招的小伙伴✿✿ヽ(°▽°)ノ✿），有面Python开发方向的，看这一个repo就够啦😘

12-class joint frequency-phase modulated SSVEP dataset for estimating online BCI performance

CNN-Former model with EEG-ME for SSVEP classification

Code to accompany our International Conference on Pattern Recognition (ICPR) paper entitled - Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.

Using DCGAN, WGAN & WGAN-GP in Augmentation of EEG data for BCIs

Using multi-task learning to capture signals simultaneously from the fovea efficiently and the neighboring targets in the peripheral vision generate a visual response map. A calibration-free user-i…

常见SSVEP信号处理算法（以空间滤波器为主）

python code for Independent Component Analysis

From scratch Python implementation of the fast ICA algorithm.

This repository is provided for replicating the canonical classifier of SSVEP signals.

This repo is created to provide some state-of-the-art (SOTA) deep learning-based classifier for SSVEP decoding.

Official Repository of 'A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces'

NeuroGNN is a state-of-the-art framework for precise seizure detection and classification from EEG data. It employs dynamic Graph Neural Networks (GNNs) to capture intricate spatial, temporal, sema…

[IEEE J-BHI-2024] A Convolutional Transformer to decode mental states from Electroencephalography (EEG) for Brain-Computer Interfaces (BCI)

[ICLR 2024 spotlight] Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

[MICCAI 2024 Acceptance] Feature Fusion Based on Mutual-Cross-Attention Mechanism for EEG Emotion Recognition

Linear Predictive Coding based Tokenizer for self-supervised learning of time series via BERT. Article: https://arxiv.org/abs/2408.07292

We propose LightCNN, a lightweight CNN architecture designed for efficient and effective Parkinson's disease classification using EEG data. Article: https://doi.org/10.48550/arXiv.2408.10457

Global Adaptive Transformer for Cross-Subject EEG Classification.

EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.