Decoding Covert Speech From EEG-A Comprehensive Review

Abstract

Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

Event-related causality in Stereo-EEG discriminates syntactic processing of noun phrases and verb phrases

“…The syntactic category of the stimulus was discriminable only by looking at the significant connections, showing the importance of restricting the topology analysis on the few significant connections. In recent years, there have been important technological and methodological advancements in perceived and imagined speech decoding (Martin et al, 2018;Panachakel & Ramakrishnan, 2021). Recent works focus on the classification of vowels (M. S. Mahmud et al, 2020; N. T. Duc & B. Lee, 2020), syllables (Archila-Meléndez et al, 2018;Brandmeyer et al, 2013;Correia et al, 2015), words (Ossmy et al, 2015;Proix et al, 2022;Vorontsova et al, 2021) and complete sentences (Chakrabarti et al, 2015;Zhang et al, 2012), distinguishing stimuli mainly at the semantic level.…”

AU-NN: ANFIS Unit Neural Network

“…In the study of imagined speech in EEG signals [3], there have been several works that intend to solve the decoding of imagined speech on brain signals [27]. Among these works there are research that aims to classify vowels [28,29,30,31], words related to directions [17,32,33,34,31], and words to answer yes/no questions [35,36].…”

Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification

“…Decoding natural language from brain signals, on the other hand, remains a major challenge. We point out that previous approaches on brain-to-text and brain-to-speech decoding (Herff et al 2015;Anumanchipalli, Chartier, and Chang 2019;Makin, Moses, and Chang 2020;Sun et al 2019;Panachakel and Ramakrishnan 2021;Nieto et al 2021;Moses et al 2021) still have limitations in terms of vocabulary size, device, and articulation dependency, etc. Previous work mainly focuses on achieving high accuracy, thus decodes sentences and words in small closed vocabularies.…”