Toward EEG Sensing of Imagined Speech

D’Zmura, Michael; Deng, Siyi; Lappas, Tom; Thorpe, Samuel G.; Srinivasan, Ramesh

doi:10.1007/978-3-642-02574-7_5

Cited by 84 publications

(73 citation statements)

References 23 publications

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“…The subvocal speech systems might be used by a person who has lost his voice permanently. A person using the subvocal speech system [21] thinks of phrases and talks to himself in silence. But the tongue and vocal cords do receive speech signals from the brain.…”

Section: Supplementary Target Bci Applications For Speech Communicationmentioning

confidence: 99%

EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

Mohanchandra

Saha

Lingaraju

2014

Brain-Computer Interfaces

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EEG based brain computer interface has emerged as a hot spot in the study of neuroscience, machine learning and rehabilitation in the recent years. A BCI provides a platform for direct communication between a human brain and a computer without the normal neurophysiology pathways. The electrical signals in the brain, because of their fast response to cognitive processes are most suitable as non-motor controlled mediation between the human and a computer. It can serve as a communication and control channel for different applications. Though the primary goal is to restore communication in severely paralyzed population, the BCI for speech communication fetches recognition in a variety of non-medical fields, the silent speech communication, cognitive biometrics and synthetic telepathy to name a few. A survey of diverse applications and principles of the BCI technology used for speech communication is discussed in this chapter. An ample evidence of speech communication used by "locked-in" patients is specified. Through the aid of assistive computer technology, they were able to pen their memoir. The current state-of-the-art techniques and control signals used for speech communication is described in brief. Possible future research directions are discussed. A comparison of indirect and direct methods of BCI speech production is shown. The direct method involves capturing the brain signals of the intended speech or speech imagery, processes the signals to predict the speech and synthesizes the speech

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Section: Supplementary Target Bci Applications For Speech Communicationmentioning

confidence: 99%

EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

Mohanchandra

Saha

Lingaraju

2014

Brain-Computer Interfaces

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“…When we average the highest TFP scores for each participant, the overall TFP score was 67.12% (TP rate= 72.38%, FP rate=3.78%) with 4s TETR and 76.67% (TP rate= 87.62%, FP rate=4.05%) with 6s TETR. 48.98 % [8,6] 44.95 % [8,12] 53.46 % [9,7] 51.00 % P3 69.95 % [11,4] 46.79 % [8,11] …”

Section: B Simulated-online Testing Evaluationmentioning

confidence: 99%

“…72.55 % [12,8] 66.86 % P4 54.06 % [9,8] 64.08 % [10,3] 48.90 % [8,7] 68.72 % [11,5] 58.94 % P5 49.51 % [8,6] 49.48 % [8,6] 61.73 % [10,6] 54.87 % [9,7] 53.90 %…”

Section: % [12 2]unclassified

“…They mostly used motor imagery (e.g., [3][4][5][6]). In addition, all the speech related EEG based BCI studies using different syllables (or syllables/vowels) that we found were focused on discrimination between various tasks, not on onset detection (i.e., idle vs. intentional state) and were cue-based approaches, not selfpaced (e.g., [7][8][9][10]), and some were ECoG studies [11,12]. This is the main novelty in our study: discriminating between soundproduction related tasks and idle (or non-specific) states for onset detection, which led to competitive results compared to systems based on typical motor-imagery tasks [5,13].…”

Section: Introductionmentioning

confidence: 98%

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A novel onset detection technique for brain–computer interfaces using sound-production related cognitive tasks in simulated-online system

Song

Sepúlveda²

2017

J. Neural Eng.

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Objective. Self-paced EEG-based BCIs (spBCIs) have traditionally been avoided due to two sources of uncertainty: 1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and 2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues.Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies-Bouldin index was used for feature selection. Classification was performed using LDA.Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% true-positive rate, 4.05% false-positive rate).Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best true-positive rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in spBCIs.

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“…DaSalla et al [14] developed a BCI using EEG for ''imaginary speech'' of the English vowels /a/ and /u/, and a no-action state as a control. The potential use of EEG as a means of silent communication has been explored by D'Zmura et al [15] and Brigham et al [16]. Subjects imagining two syllables, /ba/ and /ku/, without speaking or performing any overt actions, are assessed for feasibility and considered for subject identification [4,17].…”

Section: Related Workmentioning

confidence: 99%

A Communication Paradigm Using Subvocalized Speech: Translating Brain Signals into Speech

Mohanchandra

Saha

2016

Augment Hum Res

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Recent science and technology studies in neuroscience, rehabilitation, and machine learning have focused attention on the EEG-based brain-computer interface (BCI) as an exciting field of research. Though the primary goal of the BCI has been to restore communication in the severely paralyzed, BCI for speech communication has acquired recognition in a variety of non-medical fields. These fields include silent speech communication, cognitive biometrics, and synthetic telepathy, to name a few. Though potentially a very sensitive issue on various counts, it is likely to revolutionize the whole system of communication. Considering the wide range of application, this paper presents innovative research on BCI for speech communication. Since imagined speech suffers from quite a few factors, we have chosen to focus on subvocalized speech for the current work. The current work is considered to be the first to utilize the subvocal verbalization for EEG-based BCI in speech communication. The electrical signals generated by the human brain during subvocalized speech are captured, analyzed, and interpreted as speech. Further, the processed EEG signals are used to drive a speech synthesizer, enabling communication and acoustical feedback for the user. We attempt to demonstrate and justify that the BCI is capable of providing good results. The basis of this effort is the presumption that, whether the speech is overt or covert, it always originates in the mind. The scalp maps provide evidence that subvocal speech prediction, from the neurological signals, is achievable. The statistical results obtained from the current study demonstrate that speech prediction is possible. EEG signals suffer from the curse of dimensionality due to the intrinsic biological and electromagnetic complexities. Therefore, in the current work, the subset selection method, using pairwise cross-correlation, is proposed to reduce the size of the data while minimizing loss of information. The prominent variances obtained from the SSM, based on principal representative features, were deployed to analyze multiclass EEG signals. A multiclass support vector machine is used for the classification of EEG signals of five subvocalized words extracted from scalp electrodes. Though the current work identifies many challenges, the promise of this technology is exhibited.

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Toward EEG Sensing of Imagined Speech

Cited by 84 publications

References 23 publications

EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

A novel onset detection technique for brain–computer interfaces using sound-production related cognitive tasks in simulated-online system

A Communication Paradigm Using Subvocalized Speech: Translating Brain Signals into Speech

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