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

Mohanchandra, Kusuma; Saha, Snehanshu

doi:10.1007/s41133-016-0001-z

Cited by 27 publications

(7 citation statements)

References 23 publications

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“…Although those reports were not exactly matched to the results of classification, these discrepancies of subjective sensory perception might be related to process of imagining speech and classification results. Besides, we have not tried multiclass classification in this study, yet some attempts in multiclass classification of imagined speech have been performed by others [8, 46, 47]. These issues related to intersubject variability and multiclass systems should be considered for our future study to develop more practical and generalized BCI systems using silent speech.…”

Section: Resultsmentioning

confidence: 99%

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

Min

Kim

Park

et al. 2016

BioMed Research International

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The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems.

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Section: Resultsmentioning

confidence: 99%

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

Min

Kim

Park

et al. 2016

BioMed Research International

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“…Although the area of BCI based speech intent recognition has received increasing attention within the research community in the past few years, most research has focused on classification of individual speech categories in terms of discrete vowels, phonemes and words [5][6][7][8][9][10][11][12][13]. This includes categorization of imagined EEG signal into binary vowel categories like /a/, /u/ and rest [5][6][7]; binary syllable classes like /ba/ and /ku/ [8][9][10]14]; a handful of control words like 'up', 'down', 'left', 'right' and 'select' [13] or others like 'water', 'help', 'thanks', 'food', 'stop' [11], Chinese characters [12], etc. Such works mostly involve traditional signal processing or manual feature handcrafting along with linear classifiers (e.g., SVMs).…”

Section: Introductionmentioning

confidence: 99%

SPEAK YOUR MIND! Towards Imagined Speech Recognition with Hierarchical Deep Learning

Saha¹,

Abdul-Mageed²,

Fels³

2019

Interspeech 2019

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Speech-related Brain Computer Interface (BCI) technologies provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals. In order to infer imagined speech from active thoughts, we propose a novel hierarchical deep learning BCI system for subject-independent classification of 11 speech tokens including phonemes and words. Our novel approach exploits predicted articulatory information of six phonological categories (e.g., nasal, bilabial) as an intermediate step for classifying the phonemes and words, thereby finding discriminative signal responsible for natural speech synthesis. The proposed network is composed of hierarchical combination of spatial and temporal CNN cascaded with a deep autoencoder. Our best models on the KARA database achieve an average accuracy of 83.42% across the six different binary phonological classification tasks, and 53.36% for the individual token identification task, significantly outperforming our baselines. Ultimately, our work suggests the possible existence of a brain imagery footprint for the underlying articulatory movement related to different sounds that can be used to aid imagined speech decoding.

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“…In speech imagination, also known as Silent-Talk and Silent-Speech, the participants imagine the pronunciation of a particular vowel [2,[11][12][13], syllable [14][15][16][17], or word [18][19][20][21][22] in some defined time intervals. EEG signal during these intervals is processed to determine the imagined word [17,19,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Multiword Imagined Speech Classification for Persian Words

Bejestani

Khani

Nafisi

et al. 2022

BioMed Research International

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This study focuses on providing a simple, extensible, and multiclass classifier for imagined words using EEG signals. Six Persian words, along with the silence (or idle state), were selected as input classes. The words can be used to control a mouse/robot movement or fill a simple computer form. The data set of this study was 10 recordings of five participants collected in five sessions. Each record had 20 repetitions of all words and the silence. Feature sets consist of normalized, 1 Hz resolution frequency spectrum of 19 EEG channels in 1 to 32 Hz bands. Majority rule on a bank of binary SVM classifiers was used to determine the corresponding class of a feature set. Mean accuracy and confusion matrix of the classifiers were estimated by Monte-Carlo cross-validation. According to recording the time difference of inter- and intraclass samples, three classification modes were defined. In the long-time mode, where all instances of a word in the whole database are involved, average accuracies were about 58% for Word-Silence, 60% for Word-Word, 40% for Word-Word-Silence, and 32% for the seven-class classification (6 Words+Silence). For the short-time mode, when only instances of the same record are used, the accuracies were 96, 75, 79, and 55%, respectively. Finally, in the mixed-time classification, where samples of every class are taken from a different record, the highest performance achieved with average accuracies was about 97, 97, 92, and 62%. These results, even in the worst case of the long-time mode, are meaningfully better than random and are comparable with the best reported results of previously conducted studies in this area.

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

Cited by 27 publications

References 23 publications

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

SPEAK YOUR MIND! Towards Imagined Speech Recognition with Hierarchical Deep Learning

EEG-Based Multiword Imagined Speech Classification for Persian Words

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