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antropy

AntroPy: entropy and complexity of (EEG) time-series in Python

bend

A massively parallel, high-level programming language

bone_age_assessment

In collaboration with Dr. Liang Zhan and the 2022 Hillman Academy. This study implements a Dense Dense U-Net, a convolutional neural network, to segment hand x-rays using K-Means clusters as ground truths. A Support Vector Regression model utilizes produced segmentations to predict bone age with greater speed, precision and accuracy as radiologists

boneage

This project aims to determine through machine learning methods the bone age from digital radiographs of patients aged 0 to 228 months. The challenge can be found on the RSNA website at the following link: (https://www.rsna.org/rsnai/ai-image-challenge/RSNA-Pediatric-Bone-Age-Challenge-2017)

ct-rg-matcher

Automatic, markerless X-ray image registration

depression_prediction

This projects aims to predict depression using various supervised machine learning models

detection-of-mdd-with-eeg-signals-using-inceptiontime-model

Automated Detection of Major Depressive Disorder with EEG Signals: A Time Series Classification Using Deep Learning

diagnosing-depression-using-eeg

Depression study through EEG data

doubanmovierecommendationsystem

:movie_camera: 豆瓣电影推荐系统（Douban Movie Recommendation System）根据豆瓣电影数据以及豆瓣用户的观影和影评数据，使用基于物品的协同过滤算法对用户进行个性化推荐，并设计GUI进行用户交互。

ebooks

📖 编程书籍资源库

ecg_ssl_12lead

[IEEE BHI 2022] Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning

eeg-classification

This study aims to develop ML and DL methods which uses signals collected from electroencephalogram in application for detection of depression. We have extracted eleven statistical features form signal before feeding them to the model. We build three classifiers: Logistic Regression, Support Vector Machine, and 1-D Convolutional neural network. Our methods are tested of a dataset which comprise of signals form 30 healthy subjects and 34 MDD patients, these signals were collected from three different criteria: EC when eyes of subject are closed, EO when eyes of subject are open, and TASK when subject is doing some tasks. All three classifiers are applied on each of three types of signals, which gives a total of nine (3X3) experiments. Our results found that TASK signals given better accuracies of 88.4, 89.3, 90.21 for logistic regression, SVM and 1-D CNN respectively when compare to EC and EO signals, and also our results gave better accuracy than some of the available state-of-the-art methods

eeg-classification-model

In this project, I built a classification model to analyze EEG data and classify it into different categories. EEG data is widely used in neuroscience and medical fields, including the diagnosis of epilepsy. I used two EEG datasets to train and evaluate my model.

eeg-connectivity-estimate-with-transformers

eeg-gan_cognitivescience2023

eeg-motor-imagery-classification

EEG Motor Imagery Classification Using CNN, Transformer, and MLP

eeg_attention

神念脑电专注度算法

eeg_speech_depression_multidl

epilepsy_eeg_classification

epilepsydetection

Utilizing deep learning for improved epilepsy seizure detection using EEG signal

groupresources

A common place to compile resources of use to the research group

how-to-optim-algorithm-in-cuda

how to optimize some algorithm in cuda.

keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.

llama3-from-scratch

llama3 implementation one matrix multiplication at a time

llms-from-scratch

Implementing a ChatGPT-like LLM from scratch, step by step

metabci

MetaBCI: China’s first open-source platform for non-invasive brain computer interface. The project of MetaBCI is led by Prof. Minpeng Xu from Tianjin University, China.

msci-dataset

A multistate dataset for colposcopy image classification of cervical cancer screening

multimodal-artifact-removal-on-eog-and-ecg-artifacts-in-eeg-datasets

neuralangelo

Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 2023)

neurodsp

Digital signal processing for neural time series.