  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, 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 argued that such decoding
accuracy was overestimated due to the inherent temporal autocorrelation of EEG
signals. 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. Furthermore, the underlying pitfalls behind
overestimated decoding accuracy have not been fully explained due to a lack of
appropriate formulation. In this work, we formulate the pitfall in various EEG
decoding tasks in a unified framework. EEG data were recorded from watermelons
to remove stimulus-driven neural responses. Labels were assigned to continuous
EEG according to the experimental design for EEG recording of several typical
datasets, and then the decoding methods were conducted. The results showed the
label can be successfully decoded as long as continuous EEG data with the same
label were split into training and test sets. Further analysis indicated that
high accuracy of various BCI decoding tasks could be achieved by associating
labels with EEG intrinsic temporal autocorrelation features. These results
underscore the importance of choosing the right experimental designs and data
splits in BCI decoding tasks to prevent inflated accuracies due to EEG temporal
autocorrelation.
