Abstract
Imagined speech is a relatively new electroencephalography (EEG) neuro-paradigm, which has seen little use in Brain-Computer Interface (BCI) applications. Imagined speech can be used to allow physically impaired patients to communicate and to use smart devices by imagining desired commands and then detecting and executing those commands in a smart device. The goal of this research is to verify previous classification attempts made and then design a new, more efficient neural network that is noticeably less complex (fewer number of layers) that still achieves a comparable classification accuracy. The classifiers are designed to distinguish between EEG signal patterns corresponding to imagined speech of different vowels and words. This research uses a dataset that consists of 15 subjects imagining saying the five main vowels (a, e, i, o, u) and six different words. Two previous studies on imagined speech classifications are verified as those studies used the same dataset used here. The replicated results are compared. The main goal of this study is to take the proposed convolutional neural network (CNN) model from one of the replicated studies and make it much more simpler and less complex, while attempting to retain a similar accuracy. The pre-processing of data is described and a new CNN classifier with three different transfer learning methods is described and used to classify EEG signals. Classification accuracy is used as the performance metric. The new proposed CNN, which uses half as many layers and less complex pre-processing methods, achieved a considerably lower accuracy, but still managed to outperform the initial model proposed by the authors of the dataset by a considerable margin. It is recommended that further studies investigating classifying imagined speech should use more data and more powerful machine learning techniques. Transfer learning proved beneficial and should be used to improve the effectiveness of neural networks.
Highlights
Electroencephalography (EEG) has seen a number of high-profile advances made in recent times, like robot tracking through mind control [1] and speech synthesis from neural signals [2]
The mean accuracies for the convolutional neural network (CNN) and all of the Transferon learning (TL) methods are presented in Table 1 below
Out of all the TL methods, the best results were achieved by the first TL method, as was the case in Cooney’s study [8]
Summary
Electroencephalography (EEG) has seen a number of high-profile advances made in recent times, like robot tracking through mind control [1] and speech synthesis from neural signals [2]. One interesting area of EEG research is imagined speech. Imagined speech is the act of internally pronouncing words or letters without producing any auditory output. Recording and differentiating between these pronounced words could be crucial in allowing physically impaired patients to communicate with their caretakers in a natural way. Imagined speech is a comparatively new neuroparadigm that has received less attention than the four other paradigms (slow cortical potentials, motor imagery, P300 component, and visual evoked potentials) [3]. EEG data collection faces a number of difficulties—the main one being that the data are Computers 2020, 9, 46; doi:10.3390/computers9020046 www.mdpi.com/journal/computers
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