Classification of Phonological Categories in Imagined Speech using Phase Synchronization Measure

Panachakel, Jerrin Thomas; Ramakrishnan, A. G.

doi:10.1109/embc46164.2021.9630699

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…Thechallengesregardingimaginedspeechrecognitionareutilizingvariousdatasetsandsampling frequencies. (Panachakel & Ganesan, 2021) modeled an imagined speech recognition using the transfer learningapproach.Thefeaturesareextractedfromthemultipleinputchannelsthroughtheintroduced Magnitude-squaredcoherenceandMeanphasecoherencemethods.Dataaugmentationisdevisedfor solvingthetrainingdataissues.Finally,ResNet50isutilizedforclassificationpurposes.Theaccuracy isevaluatedtoshowtheperformanceenhancement.Thesimilarityinsomeregionsexistsduetothe failuretoconsidersomeregionsthedrawbackofthesystem. modeledanimaginedspeechrecognitionusingthedeeplearningapproach.…”

Section: Literature Reviewmentioning

confidence: 99%

Aquila-Eagle-Based Deep Convolutional Neural Network for Speech Recognition Using EEG Signals

Rathod¹,

Tiwari

Kakde

2022

International Journal of Swarm Intelligence Research

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The conventional BCI system experiences several issues such as background noise interference, lower precision rate and high cost. Hence, a novel speech recognition model which is based on the optimized Deep-CNN is proposed in this research article so as to restrain the issues related to the conventional speech recognition method. The significance of the research relies on the proposed method algorithm known as Aquila-eagle optimization, which effectively tunes the parameters of Deep-CNN. The most significant features are extracted in the feature selection process, which enhance the precision of the speech recognition model. Further unwanted noises in the EEG signals are constructively removed in the pre-processing stage to boost the accuracy of the Deep-CNN classifier.From the experimental outcomes it is demonstrated that the proposed Aquila-eagle-based DeepCNN outperformed other state-of-the-art techniques in terms of accuracy, precision, and recall with the values of 93.11%, 90.89%, and 93.11%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Aquila-Eagle-Based Deep Convolutional Neural Network for Speech Recognition Using EEG Signals

Rathod¹,

Tiwari

Kakde

2022

International Journal of Swarm Intelligence Research

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“…A BCI system is a device that can extract brain activity and process brain signals to enable computerized devices to accomplish specific purposes such as communicating or controlling prostheses (Wolpaw et al, 2002). The most commonly used BCI systems involve motor or kinaesthetic imagery (Jin et al, 2012;Milanés-Hermosilla et al, 2021;Mattioli et al, 2022), language (Panachakel and Ramakrishnan, 2021), face recognition (Kaufmann et al, 2013), and P300 detection (Azinfar et al, 2013;Guy et al, 2018;Mussabayeva et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Validation of a pictionary-based communication tool for assessing physiological needs and motivational states: the PAIN set

Proverbio

Pischedda

2023

Front. Cognit.

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This Pictionary is suitable for communicating with individual unable to interact (locked-in syndrome, LIS) through the development of Brain Computer Interface (BCI) systems. It includes 60 validated, easy-to-understand illustrated plates depicting adults in various situations affecting their physiological or psychological state. The drawings are in color and represent persons of both sexes and various ethnicities. Twenty participants were interviewed to appropriately design the Pictionary. An additional group of 50 healthy adults (25 women and 25 men) aging 18–33 years was recruited to validate the pictogram corpus. Their schooling levels ranged from middle school to master's degrees. Participants were presented with five runs of randomly mixed pictograms illustrating 12 different motivational states, including primary and secondary needs, affective states, and somatosensory sensations (with five variants for each category). They had to precisely identify the motivational category illustrated (e.g., “Feeling pain” or “Being hungry”) while also providing information about its clarity and unambiguity on a Likert scale. Statistical analyses provided evidence of the strong communicative effectiveness of the illustrations (rated on average 2.7, on a 0 to 3 scale), with an accuracy of 98.4%. The PAIN set could be a valuable communication tool for individuals with LIS, as well as any clinical population lacking verbal communication skills. Its main purpose is to generate electrophysiological markers of internal mental states to be automatically classified by BCI systems.

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“…A BCI system is a device that can extract brain activity and process brain signals to enable computerized devices to accomplish specific purposes, such as communicating or controlling prostheses. The more commonly used systems involve motor imagery (e.g., Hétu et al, 2013 ; Kober et al, 2019 ; Su et al, 2020 ; Jin et al, 2021 ; Milanés-Hermosilla et al, 2021 ; Mattioli et al, 2022 ), communication ( Blankertz et al, 2011 ; Jahangiri et al, 2019 ; Panachakel and G, 2021 ), face recognition ( Zhang et al, 2012 ; Cai et al, 2013 ; Kaufmann et al, 2013 ), or P300 detection ( Pires et al, 2011 ; Azinfar et al, 2013 ; Guy et al, 2018 ; Shan et al, 2018 ; Mussabayeva et al, 2021 ; Rathi et al, 2021 ; Leoni et al, 2022 ). Only few studies have used simultaneously BCI systems for the recognition of multiple ERP signals reflecting distinct types of mental contents, such as music ( Zhang et al, 2012 ), faces ( Cai et al, 2013 ; Li et al, 2020 ), or visual objects ( Pohlmeyer et al, 2011 ; Wang et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems

Proverbio

Tacchini

Jiang

2022

Front. Behav. Neurosci.

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ObjectiveA majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity.MethodsA total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms × 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected.ResultsClear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data.ConclusionThe data showed a high level of accuracy (p ≤ 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.

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Classification of Phonological Categories in Imagined Speech using Phase Synchronization Measure

Cited by 9 publications

References 34 publications

Aquila-Eagle-Based Deep Convolutional Neural Network for Speech Recognition Using EEG Signals

Aquila-Eagle-Based Deep Convolutional Neural Network for Speech Recognition Using EEG Signals

Validation of a pictionary-based communication tool for assessing physiological needs and motivational states: the PAIN set

Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems

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