Decoding English Alphabet Letters Using EEG Phase Information.

YiYan Wang,Yuguo Yu,Pingxiao Wang

doi:10.3389/fnins.2018.00062

YiYan Wang, Yuguo Yu + Show 1 more

Open Access

https://doi.org/10.3389/fnins.2018.00062

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Abstract

Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition.

Highlights

The past decade has witnessed great developments in brain–computer interfaces (BCIs), aiming to help severely physically impaired patients interact with the external world through tasks such as typing letters of the English alphabet on a computer for communication
We observed that the classification accuracy is around chance level for the time period
In the following, the classification accuracy values were obtained by training a support vector machine (SVM) classifier using 200 ms EEG power/phase sequences that started at the 100th millisecond after presentation of a letter

Summary

Introduction

The past decade has witnessed great developments in brain–computer interfaces (BCIs), aiming to help severely physically impaired patients interact with the external world through tasks such as typing letters of the English alphabet on a computer for communication. There is increasing evidence that the frequency-related phase pattern and power of neural oscillations may code significant sensory information relevant to human perception of the external world, especially in low-frequency bands (Luo and Poeppel, 2007; Schyns et al, 2011; Wang et al, 2012; ten Oever and Sack, 2015). Ng et al (2013) demonstrated that stimuli can be discriminated by the firing rates and phase patterns but not by the oscillation amplitude Another recent study presented evidence that syllables with varying visual-to-auditory delays are preferably processed at different oscillatory phases (ten Oever and Sack, 2015). More recent evidence suggests that decreased alpha power may be tightly correlated to the increase in the visual baseline excitability level, which may serve to improve task performance (Lange et al, 2013; Iemi et al, 2017)

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