comojin1994 / DFformer

Towards Domain Free Transformer for Generalized EEG Pre-training

Towards Domain Free Transformer for Generalized EEG Pre-training

This repository is the official implementations of DFformer in pytorch-lightning style:

S.-J. Kim, D.-H. Lee, H.-G. Kwak, and S.-W. Lee, "Towards Domain-free Transformer for Generalized EEG Pre-training," IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2024. (Accepted)

Architecture

Abstract

Electroencephalography (EEG) signals are the brain signals acquired from the non-invasive approach. Owing to its high portability and practicality, EEG signals have found extensive applications in monitoring human physiological states across various domains. In recent years, deep learning methodologies have been explored to decode the intricate information embedded within EEG signals. However, since EEG signals are acquired from humans, it has issues with acquiring enormous data for training the deep learning models. Therefore, previous research has attempted to develop pre-trained models that could show the significant performance improvement when fine-tuned when data is scarce. Nonetheless, existing pre-trained models often struggle with constraints, such as the necessity to operate within datasets of identical configurations or the need to distort the original data to apply the pre-trained model. In this paper, we propose the domain free transformer, called DFformer, for generalizing the EEG pre-trained model. In addition, we present the pre-trained model based on DFformer, capable of seamless integration across diverse datasets without necessitating architectural modification or data distortion. The proposed model achieves competitive performance across motor imagery and sleep stage classification datasets. Notably, even when fine-tuned on datasets distinct from the pre-training phase, DFformer demonstrates marked performance enhancements. Hence, we demonstrate the potential of DFformer to overcome the conventional limitations in pre-trained model development, offering robust applicability across a spectrum of domains.

1. Installation

1.1 Clone this repository

$ git clone https://github.com/comojin1994/DFformer.git

1.2 Environment setup

Create docker container and databases, logs, and checkpoints directory by under script

# docker-compose.yml
# Modify the gpu info at devices

devices:
  - /dev/nvidia0:/dev/nvidia0
  - /dev/nvidia1:/dev/nvidia1
  - /dev/nvidia2:/dev/nvidia2
  - /dev/nvidia-modeset:/dev/nvidia-modeset
  - /dev/nvidia-uvm:/dev/nvidia-uvm
  - /dev/nvidia-uvm-tools:/dev/nvidia-uvm-tools
  - /dev/nvidiactl:/dev/nvinvidiactl

$ make start_docker
$ docker exec -it dfformer bash
$ cd dfformer
$ make setup

1.3 Preparing data

After downloading the BCI Competition IV 2a & 2b, Sleep-EDF, and SHHS data, revise the data's directory in the setups/prerocess_{dataset}.py files

# 1. BCI Competition IV 2a
$ make preprocess_bci2a

# 2. BCI Competition IV 2b
$ make preprocess_bci2b

# 3. Sleep-EDF
$ make preprocess_sleepedf

# 4. SHHS
$ make preprocess_shhs

BASE_PATH = {Dataset directory}
SAVE_PATH = {Revised dataset directory}
DB_PATH = "/opt/pytorch/DFformer/databases/{dataset}.db"

2. Performance

2.1 Trained from scratch

Motor imagery datasets

Method	BCIC2a			BCIC2b
	Acc.	Kappa	F1-score	Acc.	Kappa	F1-score
DeepConvNet	0.5679	0.4239	0.5677	0.7657	0.5235	0.7656
EEGNet	0.5584	0.4113	0.5519	0.7457	0.5098	0.7457
M-ShallowConvNet	0.5783	0.4378	0.5778	0.7558	0.5167	0.7475
DFformer	0.5841	0.4455	0.5837	0.7618	0.5208	0.7552

Sleep stage classification datasets

Dataset	Method	Overall metrics			Per-class F1-score
		Acc.	Kappa	F1-score	WAKE	N1	N2	N3	REM
Sleep-EDF	DeepSleepNet	0.8190	0.7600	0.7660	0.8670	0.4550	0.8510	0.8330	0.8260
	RobustSleepNet	0.8270	0.7700	0.7530	0.8670	0.3660	0.8600	0.8640	0.8100
	U-sleep	0.7980	0.7200	0.7480	0.8240	0.4470	0.8300	0.8480	0.7900
	Proposed	0.8370	0.7778	0.7809	0.9087	0.4412	0.8657	0.8472	0.8416
SHHS	DeepSleepNet	0.8100	0.7300	0.7390	0.8540	0.4050	0.8250	0.7930	0.8190
	RobustSleepNet	0.8140	0.7400	0.7360	0.7990	0.3960	0.8310	0.8200	0.8360
	U-sleep	0.8410	0.7800	0.7690	0.8550	0.4360	0.8580	0.8330	0.8600
	Proposed	0.8389	0.7739	0.7620	0.9104	0.4420	0.8417	0.7615	0.8541

2.2 Fine-tuned results

3. Feature visualization

3.1 Visualiztion #1

• Dataset for pre-training: Sleep-EDF and SHHS
• Inference dataset: BCI Competition IV 2a

3.2 Visualization #2

• Dataset for pre-training: BCI Competition IV 2a
• Inference dataset: Sleep-EDF

3.3 Visualization #3

• Dataset for pre-training: SHHS
• Inference dataset: BCI Competition IV 2a

4. Training

4.1 Training autoencoder

Revise the options in config file

# {dataset}_config.yaml

### MANUAL ###
use_token: True
apply_cls_head: False

$ make autoencoder

4.2 Training DFformer from scratch

Revise the options in config files

# {dataset}_config.yaml

### MANUAL ###
use_token: False
apply_cls_head: True

# 1. BCI Competition IV 2a
$ make bci2a

# 2. BCI Competition IV 2b
$ make bci2b

# 3. Sleep-EDF
$ make sleepedf

# 4. SHHS
$ make shhs

5. Fine-tuning

Revise the checkpoint path and filename at config files

### PATH ###

# This is sample code
WEIGHT_PATH: "{dataset}/lightning_logs/version_0/checkpoints"
WEIGHT_FILENAME: "{checkpoint file name}.ckpt"

# 1. BCI Competition IV 2a
$ make bci2a

# 2. BCI Competition IV 2b
$ make bci2b

# 3. Sleep-EDF
$ make sleepedf

# 4. SHHS
$ make shhs