Interpreting Imagined Speech Waves with Machine Learning techniques

This work explores the possibility of decoding Imagined Speech (IS) signals which can be used to create a new design of Human-Computer Interface (HCI). Since the underlying process generating EEG signals is unknown, various feature extraction methods, along with different neural network (NN) models, are used to approximate data distribution and classify IS signals. Based on the experimental results, feed-forward NN model with ensemble and covariance matrix transformed features showed the highest performance in comparison to other existing methods. For comparison, three publicly available datasets were used. We report a mean classification accuracy of 80% between rest and imagined state, 96% and 80% for decoding long and short words on two datasets. These results show that it is possible to differentiate brain signals (generated during rest state) from the IS brain signals. Based on the experimental results, we suggest that the word length and complexity can be used to decode IS signals with high accuracy, and a BCI system can be designed with IS signals for computer interaction. These ideas, and results give direction for the development of a commercial level IS based BCI system, which can be used for human-computer interaction in daily life.

Decoding Imagined Speech and Computer Control using Brain Waves

In this work, we explore the possibility of decoding Imagined Speech brain waves using machine learning techniques. We propose a covariance matrix of Electroencephalogram channels as input features, projection to tangent space of covariance matrices for obtaining vectors from covariance matrices, principal component analysis for dimension reduction of vectors, an artificial feed-forward neural network as a classification model and bootstrap aggregation for creating an ensemble of neural network models. After the classification, two different Finite State Machines are designed that create an interface for controlling a computer system using an Imagined Speech-based BCI system. The proposed approach is able to decode the Imagined Speech signal with a maximum mean classification accuracy of 85% on binary classification task of one long word and a short word. We also show that our proposed approach is able to differentiate between imagined speech brain signals and rest state brain signals with maximum mean classification accuracy of 94%. We compared our proposed method with other approaches for decoding imagined speech and show that our approach performs equivalent to the state of the art approach on decoding long vs. short words and outperforms it significantly on the other two tasks of decoding three short words and three vowels with an average margin of 11% and 9%, respectively. We also obtain an information transfer rate of 21-bits-per-minute when using an IS based system to operate a computer. These results show that the proposed approach is able to decode a wide variety of imagined speech signals without any human-designed features.