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The ultimate goal of Brain-Computer Interface (BCI) research is to enable individuals to interact with their environment by translating their mental imagery. In this regard, a salient issue is the identification of brain activity patterns that can be used to classify intention. Using Electroencephalographic (EEG) signals as archetypical, this classification problem generally possesses two stages: (i) extracting features from collected EEG waveforms; and (ii) constructing a classifier using extracted features. With the advent of deep learning, however, the former stage is generally absorbed into the latter. Nevertheless, the burden has now shifted from trying a number of feature extraction methods to tuning a large number of hyperparameters and architectures. Among existing deep learning architectures used in BCI, Convolutional Neural Networks (CNN) have become an attractive choice. Most of the existing studies that use these networks are based on well-known architectures such as AlexNet or ResNet, use the domain knowledge to construct the final architecture or have an unclear strategy deployed for model selection. This raises the question as to whether constructing accurate CNN-based classifiers is possible using a principled model selection, with the most straightforward one being the brute-force search or, alternatively, experience and developing high intuition regarding hyperparameters combined with an ad hoc approach is the most prudent way to go about designing them. To this end, in this paper, we first define a space of hyperparameters restricted by our computing power. Then we show that an exhaustive search within this limited space of CNN hyperparameters leads to accurate classification of sensorimotor rhythms that arise during motor imagery tasks.

A Brute-Force CNN Model Selection for Accurate Classification of Sensorimotor Rhythms in BCIs