DOI: 10.5281/zenodo.11238929
title: The Phantom EEG Dataset

description: New version on https://zenodo.org/records/13341214.

When you use this dataset, please cite this paper. More information about this dataset could also be found in this paper.

Xu, X., Wang, B., Xiao, B., Niu, Y., Wang, Y., Wu, X., & Chen, J. (2024). Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals. arXiv preprint arXiv:2405.17024.

1 Metadata

Brief introduction

The present work aims to demonstrate that temporal autocorrelations (TA) significantly impacts various BCI tasks even in conditions without neural activity. We used the watermelon as the phantom head and found that we could get the pitfall of overestimated decoding performance if continuous EEG data with the same class label were split into training and test sets. More details can be found in Motivation.

As watermelons cannot perform any experimental tasks, we can reorganize it to the format of various actual EEG dataset without the need to collect EEG data as previous work did (examples in Domain Studied).

Measurement devices

Manufacturers: NeuroScan SynAmps2 system (Compumedics Limited, Victoria, Australia)

Configuration: 64-channel Ag/AgCl electrode cap with a 10/20 layout

Species

Watermelons. Ten watermelons served as phantom heads.

Domain Studied

Overestimated Decoding Performance in EEG decoding.

Following BCI datasets in various BCI tasks have been reorganized using the Phantom EEG Dataset. The pitfall has been found in four of five tasks.

-        CVPR dataset [1] for image decoding task.

-        DEAP dataset [2] for emotion recognition task.

-        KUL dataset [3] for auditory spatial attention decoding task.

-        BCIIV2a dataset [4] for motor imagery task (the pitfalls were absent due to the use of rapid-design paradigm during EEG recording).

-        SIENA dataset [5] for epilepsy detection task.

Tasks Completed

Resting State but you could reorganize it to any task in BCI.

Dataset Name

The Phantom EEG Dataset

Dataset license

Creative Commons Attribution 4.0 International

Code

The code to read the data files (.cnt) is provided in "Other". We could not add the file in this version because Zenodo demand that "you must create a new version to add, modify or delete files". We will add the file after organizing the datasets to comply with the FAIR principles in the version v2 recently.

Data information

The data will be published with following format in version v2:

-        CNT: the raw data.

-        BIDS: an extension to the brain imaging data structure for electroencephalography. BIDS primarily addresses the heterogeneity of data organization by following the FAIR principles [6].

An additional electrode was placed on the lower part of the watermelon as the physiological reference, and the forehead served as the ground site. The inter-electrode impedances were maintained under 20 kOhm. Data were recorded at a sampling rate of 1000 Hz. EEG recordings for each watermelon lasted for more than 1 hour to ensure sufficient data for the decoding task.

Each Subject (S*.cnt) contains the following information:

EEG.data: EEG data (samples X channels)

EEG.srate: Sampling frequency of the saved data

EEG.chanlocs : channel numbers (1 to 68, ‘EKG’ ‘EMG’ 'VEO' 'HEO' were not recorded)

Citation and more information

Citation will be updated after the review period is completed.

We will provide more information about this dataset (e.g. the units of the captured data) once our work is accepted. This is because our work is currently under review, and we are not allowed to disclose more information according to the relevant requirements.

All metadata will be provided as a backup on Github and will be available after the review period is completed.

 

2 Motivation

Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding, emotion recognition, auditory spatial attention detection, epilepsy detection, etc. Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of the corresponding decoding methods were doubted by some researchers, and they proposed that such decoding accuracy was overestimated due to the inherent temporal autocorrelations (TA) of EEG signals [7]–[9].

However, the coupling between the stimulus-driven neural responses and the EEG temporal autocorrelations makes it difficult to confirm whether this overestimation exists in truth. Some researchers also argue that the effect of TA in EEG data on decoding is negligible and that it becomes a significant problem only under specific experimental designs in which subjects do not have enough resting time [10], [11].

Due to a lack of problem formulation previous studies [7]–[9] only proposed that block-design should not be used to avoid the pitfall. However, the impact of TA could be avoided only when the trial of EEG was not further segmented into several samples. Otherwise, the overfitting or pitfall would still occur. In contrast, when the correct data splitting strategy was used (e.g. separating training and test data in time), the pitfall could also be avoided even when block-design was used.

In our framework, we proposed the concept of "domain" to represent the EEG patterns resulting from TA and then used phantom EEG to remove stimulus-driven neural responses for verification. The results confirmed that the TA, always existing in the EEG data, added unique domain features to a continuous segment of EEG. The specific finding is that when the segment of EEG data with the same class label is split into multiple samples, the classifier will associate the sample's class label with the domain features, interfering with the learning of class-related features. This leads to an overestimation of decoding performance for test samples from the domains seen during training, and results in poor accuracy for test samples from unseen domains (as in real-world applications).

Importantly, our work suggests that the key to reducing the impact of EEG TA on BCI decoding is to decouple class-related features from domain features in the actual EEG dataset. Our proposed unified framework serves as a reminder to BCI researchers of the impact of TA on their specific BCI tasks and is intended to guide them in selecting the appropriate experimental design, splitting strategy and model construction.

3 The rationality for using watermelon as the phantom head

We must point out that the "phantom EEG" indeed does not contain any "EEG" but records only noise, a watermelon is not a brain and does not generate any electrical signals. Therefore, the recorded electrical noises, even when amplified using equipment typically used for EEG, do not constitute EEG data when considering the definition of EEG. This is why previous researchers called it "phantom EEG". Some researchers may therefore think that it is questionable to use watermelon to get the phantom EEG.

However, the usage of the phantom head allows researchers to evaluate the performance of neural-recording equipment and proposed algorithms without the effects of neural activity variability, artifacts, and potential ethical issues. Phantom heads used in previous studies include digital models [12]–[14], real human skulls [15]–[17], artificial physical phantoms [18]–[24] and watermelons [25]–[40]. Due to their similar conductivity to human tissue, similar size and shape to the human head, and ease of acquisition, watermelons are widely used as "phantom heads".

Most works tried to use watermelon as a phantom head and found that the results analyzed using the neural signals from human subjects could not be obtained when using the phantom head, thus proving that the achieved results were indeed caused by neural signals. For example, Mutanen et.al [35] proposed that “the fact that the phantom head stimulation did not evoke similar biphasic artifacts excludes the possibility that residual induced artifacts, with the current TMS-compatible EEG system, could explain these components”.

Our work differs significantly from most previous works. It is firstly found in our work that the phantom EEG exhibits the effect of TA on BCI decoding even when only noise was recorded, indicating the inherent existence of TA in the EEG data. The conclusion we hope to draw is that some current works may not truly use stimulus-driven neural responses to obtain the overestimated decoding performance. Similar logic may be found in a neuroscience review article [41], they proposed that EEG recordings from phantom head (watermelon) remind us that background noise may appear as positive results without proper statistical precautions.

 

 

Reference

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description: # The python code would be in the files in version v1.

# We could not add the file in this version because Zenodo demand that "you must create a new version to add, modify or delete files".

# This is the python code to read the data files (.cnt)# We also provide the code to reorganize the WM-CVPR dataset

import argparseimport os.path as opimport mneimport numpy as npimport osimport pickle

def reorganize_WM(project_dir, band,save_dir):

    if not op.exists(save_dir):        os.makedirs(save_dir)

    sub_name = ['S0', 'S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9']    # fix the random seed    np.random.seed(2024)

    for sub in sub_name:        # open the .cnt file        cnt_fname = op.join(project_dir, f'{sub}.cnt')        raw = mne.io.read_raw_cnt(cnt_fname, preload=True, data_format='int32').load_data()

        # filter the WM signal        # delta: 0-4        # theta: 4-8        # alpha: 8-12        # beta: 12-32        # low_gamma: 32-45        # high_gamma: 55-95        if band == 'delta':            raw.filter(0, 4)        elif band == 'theta':            raw.filter(4, 8)        elif band == 'alpha':            raw.filter(8, 12)        elif band == 'beta':            raw.filter(12, 32)        elif band == 'low_gamma':            raw.filter(32, 45)        elif band == 'high_gamma':            raw.filter(55, 95)

        # get the raw data        data_raw = raw.get_data()        # you could delete this code, but the data would be normalized in data preprocessing        data_raw = data_raw * 1e6        channel_names = raw.ch_names        # delete the VEO, HEO, EKG, EMG channels, which did not record        del_name = ['EKG', 'EMG','VEO', 'HEO']        sel_idx = [channel_names.index(name) for name in channel_names if name not in del_name]        data_raw = data_raw[sel_idx, :]

        # reorganize the data        eegdata_raw = np.transpose(data_raw)

        eegdata = np.zeros((40, 50, 500, 128))        eeglabel = list(range(40))

        for tr in range(40):            for pic in range(50):                eegtmp = eegdata_raw[                         int((tr * 35 + pic * 0.5) * 1000 + 10000):int((tr * 35 + pic * (0.5) + 0.5) * 1000 + 10000),                         0:64]                eegdata[tr, pic, :, :] = np.concatenate((eegtmp, eegtmp,), axis=1)

        # to 32bit        eegdata = eegdata.astype(np.float32)        eeg_data_label = {'EEG': eegdata, 'label': eeglabel}        # save the eeg_data_label pt file        eeg_savedir = op.join(save_dir, f'{sub}.pkl')        with open(eeg_savedir, 'wb') as f:            pickle.dump(eeg_data_label, f)

if __name__ == '__main__':    project_dir = '/path_to_dataset/'    savedir = './WM_CVPR'    band = 'full'    reorganize_WM(project_dir, band, savedir)