# Inner speech recognition through electroencephalographic signals

arXiv: https://arxiv.org/abs/2210.06472 · HTML: https://mlsanity.com/paper/2210.06472/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications](https://mlsanity.com/paper/2001.11337/) (2020)
- [SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning](https://mlsanity.com/paper/1904.05746/) (2019)

## Built on by (most cited first, 1 of 1)

- [Electroencephalogram Emotion Recognition via AUC Maximization](https://mlsanity.com/paper/2408.08979/) (2024)

## Abstract (arXiv)

This work focuses on inner speech recognition starting from EEG signals. Inner speech recognition is defined as the internalized process in which the person thinks in pure meanings, generally associated with an auditory imagery of own inner "voice". The decoding of the EEG into text should be understood as the classification of a limited number of words (commands) or the presence of phonemes (units of sound that make up words). Speech-related BCIs provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals, improving the quality of life of people who have lost the capability to speak, by restoring communication with their environment. Two public inner speech datasets are analysed. Using this data, some classification models are studied and implemented starting from basic methods such as Support Vector Machines, to ensemble methods such as the eXtreme Gradient Boosting classifier up to the use of neural networks such as Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM). With the LSTM and BiLSTM models, generally not used in the literature of inner speech recognition, results in line with or superior to those present in the stateof-the-art are obtained.