BErt-like Neurophysiological Data Representation

Related tags

Data AnalysisBENDR
Overview

BENDR

BErt-like Neurophysiological Data Representation

A picture of Bender from Futurama

This repository contains the source code for reproducing, or extending the BERT-like self-supervision pre-training for EEG data from the article:

BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data

To run these scripts, you will need to use the DN3 project. We will try to keep this updated so that it works with the latest DN3 release. If you are just looking for the BENDR model, and don't need to reproduce the article results per se, BENDR will be (or maybe already is if I forgot to update it here) integrated into DN3, in which case I would start there.

Currently, we recommend version 0.2. Feel free to open an issue if you are having any trouble.

More extensive instructions are upcoming, but in essence you will need to either:

a)  Download the TUEG dataset and pre-train new encoder and contextualizer weights, _or_
b)  Use the [pre-trained model weights](https://github.com/SPOClab-ca/BENDR/releases/tag/v0.1-alpha)

Once you have a pre-trained model:

1) Add the paths of the pre-trained weights to configs/downstream.yml
2) Edit paths to local copies of your datasets in configs/downstream_datasets.yml
3) Run downstream.sh

Comments
  • about the loss function

    about the loss function

    Very appreciate for your contribution.i am really interested in the self training in EEG. The only question is about calculating loss function. In your paper, The calculation of the denominator uses cosine similarity between the output of the transformer and the 20 distractors and the input of the transformer. However, in the code, the calculation of the denominator uses cosine similarity between the input of the transformer and the 20 distractors, and the output of the transformer. In other word, the output and the input switch positions. Are both the calculation approaches the same? Or why did you change the calculation approache in the code? Thanks!

    opened by stickOverCarrot 2
  • About deploy downstream.yml and downstream_datasets.yml

    About deploy downstream.yml and downstream_datasets.yml

    Tranks for supplying your code. But when I follow your markdown, I meet some problems image

    This is my project files image

    This is my downstream.yml image

    This is my downstream_datasets.yml image

    opened by YoloEliwa 1
  • Pre-trained weights?

    Pre-trained weights?

    Not an issue per se, but you state the pre-trained weights for your paper are available in this repo, yet I have had a good look around and I haven't found them, nor a means of downloading them. Please can you let me know where I could find them? I'm really keen to try out this exciting architecture you've put together!

    opened by SgtWhiskeyjack 1
  • result_tracking module

    result_tracking module

    There's a reference that's in the module import: downstream.py from result_tracking import ThinkerwiseResultTracker that looks like some type of tracking code for experiments?

    opened by bencten 1
  • dropout should change

    dropout should change

    Iteration: 4%|▍ | 13/330 [00:36<16:00, 3.03s/batches, bac=0.5, Accuracy=0.51, loss=0.695, lr=1.47e-6]D:\Anaconda\envs\LGG\lib\site-packages\torch\nn\functional.py:1338: UserWarning: dropout2d: Received a 3D input to dropout2d and assuming that channel-wise 1D dropout behavior is desired - input is interpreted as shape (N, C, L), where C is the channel dim. This behavior will change in a future release to interpret the input as one without a batch dimension, i.e. shape (C, H, W). To maintain the 1D channel-wise dropout behavior, please switch to using dropout1d instead. warnings.warn("dropout2d: Received a 3D input to dropout2d and assuming that channel-wise "

    opened by zy2021314 0
  • A more detailed explanation

    A more detailed explanation

    We need to use your code for research, may I ask when you can provide detailed explanation, because we have some difficulties in understanding the code without detailed explanation.

    opened by EchizenMike 0
  • preload in downstream.yml

    preload in downstream.yml

    In the "downstream.yml" file, what is the function of the "preload"? What's mean if I specify "preload: True" or "preload: False"?

    Thank you in advance

    opened by frannfuri 0
Releases(v0.1-alpha)
Owner
GitHub Repository
Calculate multilateral price indices in Python (with Pandas and PySpark).

IndexNumCalc Calculate multilateral price indices using the GEKS-T (CCDI), Time Product Dummy (TPD), Time Dummy Hedonic (TDH), Geary-Khamis (GK) metho

Dr. Usman Kayani 3 Apr 27, 2022
vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models

gg I wasn't satisfied with any of the other available Gemini clients, so I wrote my own. Requires Python 3.9 (maybe older, I haven't checked) and opti

RAFAEL RODRIGUES 5 Jan 03, 2023
The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

Bell Eapen 14 Jan 02, 2023
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.

FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python. NOTE: Imminent drop of support of Python 2.7, 3.4. See secti

Roger Labbe 2.5k Dec 30, 2022
Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video.

Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video. You can chose the cha

2 Jul 22, 2022
Data analysis and visualisation projects from a range of individual projects and applications

Python-Data-Analysis-and-Visualisation-Projects Data analysis and visualisation projects from a range of individual projects and applications. Python

Tom Ritman-Meer 1 Jan 25, 2022
This tool parses log data and allows to define analysis pipelines for anomaly detection.

logdata-anomaly-miner This tool parses log data and allows to define analysis pipelines for anomaly detection. It was designed to run the analysis wit

AECID 32 Nov 27, 2022
Python-based Space Physics Environment Data Analysis Software

pySPEDAS pySPEDAS is an implementation of the SPEDAS framework for Python. The Space Physics Environment Data Analysis Software (SPEDAS) framework is

SPEDAS 98 Dec 22, 2022
Detecting Underwater Objects (DUO)

Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by ad

27 Dec 12, 2022
follow-analyzer helps GitHub users analyze their following and followers relationship

follow-analyzer follow-analyzer helps GitHub users analyze their following and followers relationship by providing a report in html format which conta

Yin-Chiuan Chen 2 May 02, 2022
An implementation of the largeVis algorithm for visualizing large, high-dimensional datasets, for R

largeVis This is an implementation of the largeVis algorithm described in (https://arxiv.org/abs/1602.00370). It also incorporates: A very fast algori

336 May 25, 2022
In this tutorial, raster models of soil depth and soil water holding capacity for the United States will be sampled at random geographic coordinates within the state of Colorado.

Raster_Sampling_Demo (Resulting graph of this demo) Background Sampling values of a raster at specific geographic coordinates can be done with a numbe

2 Dec 13, 2022
Python ELT Studio, an application for building ELT (and ETL) data flows.

The Python Extract, Load, Transform Studio is an application for performing ELT (and ETL) tasks. Under the hood the application consists of a two parts.

Schlerp 55 Nov 18, 2022
Leverage Twitter API v2 to analyze tweet metrics such as impressions and profile clicks over time.

Tweetmetric Tweetmetric allows you to track various metrics on your most recent tweets, such as impressions, retweets and clicks on your profile. The

Mathis HAMMEL 29 Oct 18, 2022
Generate lookml for views from dbt models

dbt2looker Use dbt2looker to generate Looker view files automatically from dbt models. Features Column descriptions synced to looker Dimension for eac

lightdash 126 Dec 28, 2022
Falcon: Interactive Visual Analysis for Big Data

Falcon: Interactive Visual Analysis for Big Data Crossfilter millions of records without latencies. This project is work in progress and not documente

Vega 803 Dec 27, 2022
A program that uses an API and a AI model to get info of sotcks

Stock-Market-AI-Analysis I dont mind anyone using this code but please give me credit A program that uses an API and a AI model to get info of stocks

1 Dec 17, 2021
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
Create HTML profiling reports from pandas DataFrame objects

Pandas Profiling Documentation | Slack | Stack Overflow Generates profile reports from a pandas DataFrame. The pandas df.describe() function is great

10k Jan 01, 2023
Churn prediction with PySpark

It is expected to develop a machine learning model that can predict customers who will leave the company.

3 Aug 13, 2021