Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features

Abstract

The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.

Analysis of speech related EEG signals using emotiv epoc+ headset, fast fourier transform, principal component analysis, and K-nearest neighbor methods

“…13 Most recently, in 2017, a team led by Nguyen, Karavas, and Artemiadis used a BrainProducts ActiCHamp EEG System to infer imagined speech with a novel normalization method based on covariance matrix descriptors, more specifically, using Riemannian manifold features. 14 The results of this research had extremely high accuracy, reaching 70 percent for a three-word test and 95 percent for a two word test. The high accuracy rates achieved demonstrated the potential for success associated with developing novel algorithms to process EEG data.…”

“…8 In addition, future work should include investigating the effect of the use of a normalization process such as the one mentioned earlier that was proposed. 14…”

“…These five training sets were then normalized and combined via covariance matrix descriptors such as Riemannian manifold features, similar to the novel normalization process described. 14…”

Decoding Imagined Speech using Wavelet Features and Deep Neural Networks

“…In 2017, Nguyen, Karavas and Artemiadis [6] came up with an approach based on Riemannian manifold features for classifying four different sets of prompts: 1) Vowels (/a/, /i/ and /u/ ). 2) Short words ("in" and "out").…”

“…This is possible because of the high correlation present between the signals of various channels [13]. The following 11 channels only have been chosen to be used in our work, based on the involvement of the underlying brain regions in the production of speech [14], [15]: 1) 'C4': postcentral gyrus 2) 'FC3': premotor cortex 3) 'FC1': premotor cortex 4) 'F5': inferior frontal gyrus, Broca's area 5) 'C3': postcentral gyrus 6) 'F7': Broca's area 7) 'FT7': inferior temporal gyrus 8) 'CZ': postcentral gyrus 9) 'P3': superior parietal lobule 10) 'T7': middle temporal gyrus, secondary auditory cortex 11) 'C5': Wernicke's area, primary auditory cortex This choice of channels is also backed by the common spatial patterns (CSP) analysis on imagined speech v/s rest state EEG data [6].…”