Brain-computer interface technology for speech recognition: A review

Al-Saleh, Mashael; Arvaneh, Mahnaz; Christensen, Heidi; Moore, Roger K.

doi:10.1109/apsipa.2016.7820826

Cited by 15 publications

(3 citation statements)

References 26 publications

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“…The decoding of speech from brain signals has attracted the attention of researchers in recent years (Chakrabarti et al, 2015 ; AlSaleh et al, 2016 ; Herff and Schultz, 2016 ). A device that can directly translate brain signals into texts that describe a person's thoughts may help people with disabilities and verbal communication deficits and enable a new communication channel with the outside world.…”

Section: Introductionmentioning

confidence: 99%

Speech Recognition via fNIRS Based Brain Signals

Liu

Ayaz

2018

Front. Neurosci.

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In this paper, we present the first evidence that perceived speech can be identified from the listeners' brain signals measured via functional-near infrared spectroscopy (fNIRS)—a non-invasive, portable, and wearable neuroimaging technique suitable for ecologically valid settings. In this study, participants listened audio clips containing English stories while prefrontal and parietal cortices were monitored with fNIRS. Machine learning was applied to train predictive models using fNIRS data from a subject pool to predict which part of a story was listened by a new subject not in the pool based on the brain's hemodynamic response as measured by fNIRS. fNIRS signals can vary considerably from subject to subject due to the different head size, head shape, and spatial locations of brain functional regions. To overcome this difficulty, a generalized canonical correlation analysis (GCCA) was adopted to extract latent variables that are shared among the listeners before applying principal component analysis (PCA) for dimension reduction and applying logistic regression for classification. A 74.7% average accuracy has been achieved for differentiating between two 50 s. long story segments and a 43.6% average accuracy has been achieved for differentiating four 25 s. long story segments. These results suggest the potential of an fNIRS based-approach for building a speech decoding brain-computer-interface for developing a new type of neural prosthetic system.

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Section: Introductionmentioning

confidence: 99%

Speech Recognition via fNIRS Based Brain Signals

Liu

Ayaz

2018

Front. Neurosci.

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“…In these works, the subject imagines a high pitch sound as well as a siren-like sound, and this is used to trigger an action in an asynchronous BCI. In the study of imagined speech, there have been several works that aim to classify words or vowels [5,[15][16][17]. These works have shown promising results in this field.…”

Section: Related Workmentioning

confidence: 99%

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

Hernández-Del-Toro

Reyes-García

Villaseñor-Pineda

2021

Biomedical Signal Processing and Control

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An asynchronous Brain-Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined speech. In order to correctly implement these types of BCI, we must be able to detect from a continuous signal, when the subject starts to imagine words. In this work, five methods of feature extraction based on wavelet decomposition, empirical mode decomposition, frequency energies, fractal dimension and chaos theory features are presented to solve the task of detecting imagined words segments from continuous EEG signals as a preliminary study for a latter implementation of an asynchronous BCI based on imagined speech. These methods are tested in three datasets using four different classifiers and the higher F 1 scores obtained are 0.73, 0.79, and 0.68 for each dataset, respectively. This results are promising to build a system that automatizes the segmentation of imagined words segments for latter classification.

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“…It is also very common to use algorithms such as Common Spatial Filtering (CSP), to classify the signals or to use its output as features to feed further classifiers [5], [9]. Some of the most common classifiers used in BCI systems are Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVM) and K-Nearest Neighbours (KNN) [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Interval-Valued Aggregation Functions Based on Moderate Deviations Applied to Motor-Imagery-Based Brain–Computer Interface

Fumanal-Idocin

Takáč

Fernández

et al. 2022

IEEE Trans. Fuzzy Syst.

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In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two Motor-Imagery Brain Computer Interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation function and, in particular, we study those interval-valued moderate deviation functions which preserve the width of the input intervals. In order to apply them in a MI-BCI system, we first use fuzzy implication operators to measure the uncertainty linked to the output of each classifier in the ensemble of the system, and then we perform the decision making phase using the new interval-valued aggregation functions. We have tested the goodness of our proposal in two MI-BCI frameworks, obtaining better results than those obtained using other numerical aggregation and interval-valued OWA operators, and obtaining competitive results versus some non aggregation-based frameworks.

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Brain-computer interface technology for speech recognition: A review

Cited by 15 publications

References 26 publications

Speech Recognition via fNIRS Based Brain Signals

Speech Recognition via fNIRS Based Brain Signals

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals

Interval-Valued Aggregation Functions Based on Moderate Deviations Applied to Motor-Imagery-Based Brain–Computer Interface

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