victorywys
commented
7 months ago Many thanks for your interests in our paper. Sorry for the late reply because it took us some time to check the material you provided.
Choice of dataset and fair comparison with baselines.
We choose to use SEED as the dataset for our experiment because 1) SEED has a very different channel configuration with the large pretraining dataset (TUEG) while being reasonably large as a downstream task. 2) SEED seems to be widely used by previous studies thus it's convenient to make comparison. 3) SEED provides a DE feature officially which can reduce the burden of temporal modeling while focusing on transferring across channel configurations.
We indeed use the DE feature provided officially in our experiments. You can find the preprocess code here which is merely a transformation of the data format. This setting is shared across all the baselines used in our experiments to ensure fair comparison.
Concerning the SEED "Moving Average Problem"
Upon careful examination of the study "The Perils and Pitfalls of Block Design for EEG Classification Experiments" (hereinafter referred to as the "Question Paper" or QP) you provide, we have identified a primary concern related to the block design methodology utilized in the referenced study, "Deep Learning Human Mind for Automated Visual Classification" (hereinafter referred to as the "Origin Paper" or OP).
In the Origin Paper, the authors implemented an experimental protocol wherein 40 object classes from the ImageNet database, with 50 images per class, were presented as visual stimuli to six participants during EEG recordings. A block design was used, whereby each participant was exposed to 40 blocks of stimuli, each block comprising 50 images from a single object class, ensuring each image was presented exactly once. Consequently, all participants viewed the same set of 2,000 images in total. (Adapted from the Question Paper)
The Question Paper subsequently proposed a series of experiments to challenge the Origin Paper's findings, suggesting that the reported high accuracy might be attributable to the model "learning arbitrary long-term static mental states during a block rather than dynamic brain activity." This assertion was based on the observation that trials sharing the same label were derived from identical blocks. Hence, using moving average is making learning long-term static mental states easier, which is unreasonable for OP.
However, in SEED experiment, each subject takes 15 trails. One trial contains a movie clip with a label in one of three [netural, positive, negative]. The order of trails is near random. The training/test split is 9/6. One training sample and one validation sample with the same labels come from different trials, Hence we are not learning features for static mental states within one trial. Though it conducts moving average within one trial, it tends to eliminate the noise but not extract features for static mental states. And to the best of our understanding, information in the test set will not be exposed during training.
We hope we correctly understand and properly addressed your concerns. We welcome constructive feedback and are open to adjustments based on further insights and discussions, thus we leave this issue open for a while in case you would like to provide more details about the concern or have further questions.
sylyoung
For the results reported in the paper [Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling], the results are exceedingly high. Did you use the moving averaged features provided by SEED datasets? If so, the subject-dependent experiment becomes very unreasonable, because such methods generally assumes the full trial is of the same class, basically data leakage problem as denoted in The perils and pitfalls of block design for EEG classification experiments.