Distinción de estados de actividad e inactividad lingüística para interfaces cerebro computadora

Pascual, Moctezuma; Alfredo, Luis

doi:10.13140/rg.2.2.10145.10086

Cited by 1 publication

(3 citation statements)

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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%

“…To reduce noise and to remove artifacts from the signals, the Common Average Reference (CAR) method is applied. This method is chosen because it has also been used in other works that deal with noise and artifacts in EEG signals [5,6,15,16,20,21].…”

Section: Signal Cleaningmentioning

confidence: 99%

“…With this, we obtain 5 sets of coefficients per channel signal. The wavelet mother used was bior2.2 because it has shown desirable results in previous works that deal with imagined speech [16,21].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%