An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation

Panachakel, Jerrin Thomas; Vinayak, Nandagopal Netrakanti; Nunna, Maanvi; Ramakrishnan, A. G.; Sharma, Kanishka

doi:10.1007/978-981-15-3992-3_22

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…In our own experiments reported in Panachakel et al ( 2020b ), we have observed that the EEG signatures become more prominent across multiple imaginations in the same trial but deteriorate across multiple trials in the same recording session.…”

Section: Data Acquisitionsupporting

confidence: 57%

“…Although studies (Ikeda et al, 2012 ) have shown that the excitation of the primary motor cortex is higher when auditory and visual cues are used, the practical benefit of such a system, especially in the field of rehabilitation is limited. This is also true for the use of somatosensory stimuli in motor imagery as in Panachakel et al ( 2020b ).…”

Section: Data Acquisitionmentioning

confidence: 83%

“…These are (1) auditory (Brigham and Kumar, 2010 ; Deng et al, 2010 ; Min et al, 2016 ; Koizumi et al, 2018 ); (2) visual (Wang et al, 2014 ; Sereshkeh et al, 2017a ; Jahangiri et al, 2018 ; Koizumi et al, 2018 ); and (3) a combination of auditory and visual cues (Zhao and Rudzicz, 2015 ; Coretto et al, 2017 ; Nguyen et al, 2017 ; Watanabe et al, 2020 ). Although somatosensory cues have been used for motor imagery (Panachakel et al, 2020b ), no such work has been reported for speech imagery.…”

Section: Data Acquisitionmentioning

confidence: 99%

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Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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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.

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Section: Data Acquisitionsupporting

confidence: 57%

Section: Data Acquisitionmentioning

confidence: 83%

Section: Data Acquisitionmentioning

confidence: 99%

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Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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“…An enormous body of research has been conducted over the past decade aiming to convert human brain signals to speech. Although experiments have shown that the excitation of the central motor cortex is elevated when visual and auditory cues are employed, the functional benefit of such a method is limited [ 1 ]. Imagined speech, sometimes called inner speech, is an excellent choice for decoding human thinking using the brain–computer interface (BCI) concept.…”

Section: Introductionmentioning

confidence: 99%

Imagined Speech Classification Using EEG and Deep Learning

2023

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In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The long-short term memory recurrent neural network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: up, down, left, and right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration was implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based brain–computer interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50%, and 92.62% for precision, recall, and F1-score, respectively.

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“…Enormous research has been done over the past decade aiming to convert human brain signals to speech. Although experiments have shown that the excitation of the central motor cortex is elevated when visual and auditory cues are employed, the functional benefit of such a method is limited [1]. Inner speech, sometimes called imagined speech, is an excellent choice for generating human thinking using the Brain-Computer Interface (BCI) concept.…”

Section: Introductionmentioning

confidence: 99%

Inner Speech Classification Using EEG and Deep Learning

Abdulghani¹,

Walters²,

Abed³

2023

Preprint

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In this paper, we propose an inner speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel Electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer number of sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: Up, Down, Left, and Right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration has been implemented for each individual command in the EEG datasets. The proposed inner speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy inner speech-based Brain-Computer Interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, respectively.

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An Improved EEG Acquisition Protocol Facilitates Localized Neural Activation

Cited by 10 publications

References 17 publications

Decoding Covert Speech From EEG-A Comprehensive Review

Decoding Covert Speech From EEG-A Comprehensive Review

Imagined Speech Classification Using EEG and Deep Learning

Inner Speech Classification Using EEG and Deep Learning

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