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Brain Waves

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

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Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

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Decoding Covert Speech From EEG by Functional Areas Spatio-Temporal Transformer.

Muyun Jiang, Wei Zhang, Yi Ding

IEEE Journal of Biomedical and Health Informatics

|January 12, 2026

Summary

This summary is machine-generated.

Researchers decoded covert speech from electroencephalogram (EEG) signals using a novel transformer model. This breakthrough offers interpretable insights into neural representations of imagined speech.

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Last Updated: Jun 24, 2026

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

Area of Science:

Neuroscience
Cognitive Science
Signal Processing

Background:

Decoding covert speech from electroencephalogram (EEG) is difficult due to limited understanding of neural pronunciation mapping and low signal-to-noise ratios.
Covert speech, the imagination of speaking without audible sound or movement, presents unique challenges for neural decoding.

Purpose of the Study:

To develop an effective framework for decoding covert speech from EEG signals.
To investigate the neural mechanisms underlying covert speech production and identify discriminative neural features.

Main Methods:

Developed a large-scale multi-utterance speech EEG dataset from 57 participants.
Introduced the Functional Areas Spatio-temporal Transformer (FAST) framework to process EEG signals.
Utilized transformer architecture for sequence encoding of EEG data.

Main Results:

Identified distinct and interpretable speech neural features through FAST-generated activation maps.
Visualized neural activation across frontal and temporal brain regions during covert speech.
Demonstrated the effectiveness of the FAST framework in speech decoding from EEG.

Conclusions:

This study provides the first interpretable evidence for speech decoding from EEG.
The FAST framework offers new insights into the discriminative features of covert speech neural representations.
The developed dataset and framework pave the way for future research in brain-computer interfaces for speech.