p300-based-brain-computer-interface-speller-performance-estimation

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI ...

Kansas State University

Affective states play an important role in human behavior and decision-making. In recent years, several affective brain-computer interface (aBCI) studies have focused on developing an emotion classifier based on elicited emotions within the user. However, it is difficult to achieve consistency in elicited emotions across populations, which can lead to dataset imbalances. The experimental design presented in this paper seeks to avoid consistency issues by asking the participant to classify the emotion portrayed in images of facial expressions, rather than their own emotions. Priming is also a common technique used in psychology studies that is known to influence emotional perception. To improve participant accuracy, we investigated matching and mis-matched word priming for the facial expression images. Electro-encephalogram (EEG) data were used to generate images fed into a classifier based on the Big Transfer model, BiT-M R101x1. The primed images resulted in higher classification accuracy overall. Further, by building different classifier models for both mis-matched primed images and matching primed images, we were able to achieve classification accuracies above 90%. We also provided the classifier with the true labels of the photographs instead of the labels generated by the participants and achieved similar results. The experimental paradigm of measuring brain activity during the emotional classification of another individual provides consistently high, balanced classification accuracies.

The Effects of Word Priming on Emotion Classification from Neurological Signals.

Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.

Article	Year
The Effects of Word Priming on Emotion Classification from Neurological Signals. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\| PubMed ID: 36085617	2022
Applying Big Transfer-based classifiers to the DEAP dataset. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\| PubMed ID: 36086186	2022

Taylor Sweet

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