97 skills found · Page 4 of 4
tziporaziegler / Brain Controlled ArmArduino-based robotic arm controlled by brain waves. Uses EEG data extracted from a NeuroSky board to control arm movement. 🤯
m-granat / Cnn For Eeg TscElectroencephalography (EEG) is an effective and non-invasive way to capture electric activity of the brain. EEG time series classification is a very important problem in neuroscience as a lot of EEG practical applications, such as medical diagnostics and brain–computer interfaces, depend on the quality of classification results. Several effective classification methods that use deep neural networks were developed in recent years, and convolutional neural networks (CNN) have shown exceptional results in many studies. The aim of this project is to investigate if EEG time series can be classified according to the types of attention (mental or sensory) that was used by research participants during the experiment. Deep multi-scale convolutional neural network is used as a classification model. The results have shown that CNN is indeed capable of solving such classification tasks: 98% accuracy was obtained on validation dataset. The obtained results suggest that CNN capabilities in extracting features from time series data is a perspective field for further research.
RutujaaP / Evaluation Of Boosting Algorithms For P300 Detection In EEG SignalsBrain-Computer Interfaces have impacted the lives of many, especially those whose mobility and ability to speak are affected. It is able to do so by bridging the gap between thoughts and devices. One of its most popular applications, the P300 Speller, is a powerful aid that allows the patients to regain a certain level of autonomy. Detection of a P300 peak and character identification are the two major components of a P300 speller. In this study, the first component of P300 speller is covered. Various conventional learning algorithms like Support Vector Machine, Discriminant Analysis, Neural Network and their variants have been used in previous studies. These methods have limitations: some are prone to overfitting; others require a large amount of training data, while there are some limitations that necessitate complicated computing thus making them less favorable for real-time analysis. Boosting algorithms are very less explored in the field of Electroencephalography (EEG) and less prone to most of the limitations of these conventional models. This paper evaluates the performances of LightGBM and CatBoost on the dataset used in the competition BCI NER 2015 on Kaggle. These algorithms have recently gained popularity and have proven to be powerful. Further, they are compared with the performances of XGBoost and AdaBoost and a maximum 1 Score of 0.84 was achieved using LightGBM as a classifier.
rkobler / Hearhigh-variance electrode artifact removal (HEAR) algorithm
davidlee-ca / EpileptisentryReal-time electroencephalography (EEG) signal analysis for monitoring abnormal brain activity at scale
nik-sm / Bci Disc Models"Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models"
biomedical-signal-processing / Multi Scored SleepMulti-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring
mccarthy-m-g / Mccarthy EEGNetworkVariants 2024Materials and source code for my MSc thesis: "Studying Network Variants With Electroencephalography"
TheChuckster / EEG BCIBrain-Computer Interface Charles Moyes (cwm55) and Mengxiang Jiang (mj294) We built a robust Brain-Computer Interface (BCI) using single-channel electroencephalography (EEG) with an AVR microcontroller, and we were able to play Pong using our brain waves (and monitor/record our sleep).
PedroFerreiradaCosta / NeuroadaptiveEEGImplementation code for neuroadaptive electroencephalography. The mother-infant paradigm in infant research was used as a proof-of-concept
ML4PNP / MEGaNormMEGaNorm is a Python package for normative modeling on MEG and EEG data.
Seba3995 / EEG Web AppInteractive web application developed with Streamlit to visualize and analyze EEG (Electroencephalography) signals. It allows uploading and processing EEG data to analyze complexity and entropy.
alborzrs / EEG BCI DataAnalysisFeature extraction (autoregressive and wavelet transform features) and epoching (from vhdr files and using marker) codes in MATLAB for analyzing EEG (electroencephalography) data for brain-computer interfaces (BCIs).
Angelawork / EEG Foundation Model LiNC Lab COMP396🧠This project collaborates with researchers at Mila to develop pre-trained transformer models for decoding electroencephalography (EEG) data signals, aiming to establish a robust framework for neural population dynamics analysis.🚀
konspatl / DL EEGA Deep Learning Library for State-Based EEG Analysis
zal-ghiffari / Pluto PolygraphPluto Polygraph is a web-based lie detector application that uses a brainwave headset to pick up EEG (Electroencephalography) signals in the brain. Pluto Polygraph uses Deep Learning technology to perform the detection process with the Long-Short Term Memory (LSTM) algorithm. The model on the Pluto Polygraph knows with a dataset the human brain's EEG signals.
Mostafa-ashraf19 / BrainWheelThe prototype consists of a brain-computer interface (BCI) system that enables users to control the wheelchair’s motorized motion by simply gazing at flashing buttons on an assistive screen, each of which represents a control command, without having to move a single muscle. The wheelchair moves on its own based on the analysis of the recorded Electroencephalography (EEG) signals.
saraMtd / ADHD Dataset**Sample** : * Women (n = 57) * Men (n = 39) * Adhd subtype : hyperactive (n = 2), inattentive (n = 48), mixed (n = 46) ### Types of measures **Conners questionnaire** : standardized questionnaire. Comprizes 66 items about ADHD symptoms and behaviors. Answers are given using a Likert scale (0 = not at all/never and 3 = very often/very frequent). The items are compiled into 4 scales; * inattention/memory (IM) * hyperactivity/restlessness (HR) * impulsivity/emotional lability (IE) * problems with self concept (SC) (refers to self esteem). These four scores are used as the 4 self report symptoms measures. Test-retest correlation for 18-29 years old ranges from 0,8 to 0,92 depending on items. **IVA-II** : Behavioral test. Participants are presented with visual and auditive stimuli (numbers). If the stimulus is 1, whether it is visual or auditive, subjects must click as quickly as possible. If the stimulus is 2, whether it is visual or auditive, subjects must refrain from clickling. Stimuli are presented in a randomized order and at random time. 2 main scales are extracted, comprising 2 subscales each. 1st main scale is Attention Quotient (AQ) and its subscales are AQ auditive and AQ visual. 2nd main scale is Response Control Quotient (RCQ) and its subscales are RCQ auditive and RCQ visual. **Electroencephalography (EEG)** : 19 electrodes caps were used, positioned according to the 10-20 international system and referenced to both ear lobes. Recordings lasted 5 minutes, were participants were instructed to be as still as possible and to keep their eyes opened. The Mitsar System 201 and WinEEG (Mitast) softwares were used for recording. Test-retest and split-half correlations were higher than 0,9.
OpenNeuroDatasets / Ds004796OpenNeuro dataset - A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database
MattiStenroos / Trapmusic PythonTRAP MUSIC algorithm for MEG/EEG multi-source localization
