Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network

Ghosh, Rajdeep; Sinha, Nidul; Phadikar, Souvik

doi:10.1002/9781119857655.ch10

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Subsequently, a manual inspection is conducted to identify and eliminate additional abnormal segments within the EEG signal. Before being fed into the DNN model, the EEG signals are split into four subbands through four bandpass filters: the delta band (1-4 Hz), theta band (4-8 Hz), alpha band (8-13 Hz), and beta band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz). The dataset is available at https://ieee-dataport.org/documents/nju-auditory-attention-dec oding-dataset.…”

Section: B Eeg Signal Preprocessingmentioning

confidence: 99%

Electroencephalogram-based Multiclass Auditory Attention Decoding of Attended Speaker Direction

Zhang,

Lu,

Lin

et al. 2023

Preprint

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<p>Decoding the directional focus of an attended speaker from listeners’ electroencephalogram signals is an important part of a practical brain-computer interface device aimed at improving the quality of life for individuals with hearing impairment. Existing works focus on binary directional focus decoding, i.e., determining whether the attended speaker is on the left or right side of the listener. However, the information brought by the binary decoding to the subsequent speech processing algorithm is limited in practical applications, and more precise decoding of the exact direction of the attended speaker is desired. In this paper, we first present a new dataset with 15 alternative speaker directions, and then demonstrate the feasibility of multiclass directional focus decoding of attended speakers by applying our recently proposed learnable spatial mapping (LSM) module, the benefit of which has already been proven in binary decoding scenarios. Apart from combining the LSM module with the convolutional neural network (CNN), we further validate its benefit by combining it with the spectro-spatial-temporal convolutional recurrent network (CRN), a recently proposed state-of-the-art model for binary directional focus decoding. The proposed LSM-CNN and LSM-CRN models achieve a noteworthy decoding accuracy of 85.7% and 87.5%, respectively, in the presented subject-independent dataset with a decision window length of 1 second. Comprehensive experiments not only substantiate the advantages attributed to the LSM module, but also verify the influence of the decision window length, the distraction caused by interfering speakers, and the contribution of different subband of EEG signals.</p>

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Section: B Eeg Signal Preprocessingmentioning

confidence: 99%

Electroencephalogram-based Multiclass Auditory Attention Decoding of Attended Speaker Direction

Zhang,

Lu,

Lin

et al. 2023

Preprint

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show abstract

“…Scalp electroencephalography (EEG) has also been recently utilised for decoding vowels, with classification accuracy of 68.9% for five Bengali vowels [35]. Another recent study reached 92.2% accuracy, but for three vowels only: a, im, and u [36].…”

Section: Introductionmentioning

confidence: 99%

Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces

Tankus,

Rosenberg,

Ben-Hamo

et al. 2024

J. Neural Eng.

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Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding.Approach. We intraoperatively recorded single neuron activity in the left Vim of 8 neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space.Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other.Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.

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A BCI system for imagined Bengali speech recognition

Hossain,

Das,

Khan

et al. 2023

Machine Learning with Applications

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Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network

Cited by 3 publications

References 20 publications

Electroencephalogram-based Multiclass Auditory Attention Decoding of Attended Speaker Direction

Electroencephalogram-based Multiclass Auditory Attention Decoding of Attended Speaker Direction

Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces

A BCI system for imagined Bengali speech recognition

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