Time Domain Analysis of EEG to Classify Imagined Speech

Iqbal, Sadaf; Shanir, P.P. Muhammed; Khan, Yusuf Uzzaman; Farooq, Omar

doi:10.1007/978-81-322-2523-2_77

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…Fourth-order Daubechies were used to achieve the results presented in [56] and were therefore used here. A feature vector was constructed using the RWE of decomposition levels D2 (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32), D3 (8-16 Hz), D4 (4-8 Hz), D5 (2-4 Hz), and A5 (<2 Hz), for each channel. This resulted in a 30-element feature vector for each trial.…”

Section: Benchmark Machine Learning Classifiersmentioning

confidence: 99%

“…Here, six frequency bands are used to construct the filter bank. These are delta (2-4 Hz), theta (4-8 Hz), mu (8-12 Hz), lower beta (12-18 Hz), upper beta (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28), and gamma (28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). Three HPs were selected for optimization, i.e., (1).…”

Section: Benchmark Machine Learning Classifiersmentioning

confidence: 99%

“…Non-invasive studies using magnetoencephalogram [ 16 ] and EEG [ 17 ] have demonstrated potential for decoding speech using these technologies. Several studies have used the advantage of non-invasive recording through EEG to investigate imagined speech as a communicative paradigm for BCIs (e.g., [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]). Traditional approaches to BCIs require feature extraction and classification algorithms designed to decode a specific control signal.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Cooney

Korik

Folli

et al. 2020

Sensors

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Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10–7, chance: 16.67%; vowels: 30.00%, p < 1 × 10–7, chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech.

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Section: Benchmark Machine Learning Classifiersmentioning

confidence: 99%

Section: Benchmark Machine Learning Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Cooney

Korik

Folli

et al. 2020

Sensors

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“…This process can be carried on the time domain, frequency domain, and spatial domain. In the time domain, the feature extraction process is often done through statistical analysis, obtaining statistical features such as standard deviation (SD), root mean square (RMS), mean, variance, sum, maximum, minimum, Hjorth parameters, sample entropy, autoregressive (AR) coefficients, among others (Riaz et al, 2014 ; Iqbal et al, 2016 ; AlSaleh et al, 2018 ; Cooney et al, 2018 ; Paul et al, 2018 ; Lee et al, 2019 ). On the other hand, the most common methods used to extract features from the frequency domain include Mel Frequency Cepstral Coefficients (MFCC), Short-Time Fourier transform (STFT), Fast Fourier Transform (FFT), Wavelet Transform (WT), Discrete Wavelet Transform (DWT), and Continuous Wavelet Transform (CWT) (Riaz et al, 2014 ; Salinas, 2017 ; Cooney et al, 2018 ; Garćıa-Salinas et al, 2018 ; Panachakel et al, 2019 ; Pan et al, 2021 ).…”

Section: Feature Extraction Techniques In Literaturementioning

confidence: 99%

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Lopez-Bernal

Balderas

Ponce

et al. 2022

Front. Hum. Neurosci.

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Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

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“…Speech-BCI or neural silent speech interface (NSSI), a novel technological paradigm that aims to convert neural signals to speech (text, acoustics, or articulatory parameters) and then drive a text-or articulatory-to-speech synthesizer [10], has been recently demonstrated to be possible from either invasive or non-invasive neural signals [11][12][13]. Although a few studies on speech decoding based EEG-BCIs have been investigated such as "yes" or "no" classification [14], binary phoneme [15] or syllable classification [16,17], they suffer from intermediate performance possibly due to the low spatial resolution and low signal-to-noise ratio of EEG signals. Invasive Electrocorticography (ECoG) and non-invasive Magnetoencephalography (MEG) have recently shown a higher potential in speech decoding [11][12][13][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Towards a Speaker Independent Speech-BCI Using Speaker Adaptation

et al. 2019

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Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) can cause locked-in-syndrome (fully paralyzed but aware). Brain-computer interface (BCI) may be the only option to restore their communication. Current BCIs typically use visual or attention correlates in neural activities to select letters randomly displayed on a screen, which are extremely slow (a few words per minute). Speech-BCIs, which aim to convert the brain activity patterns to speech (neural speech decoding), hold the potential to enable faster communication. Although a few recent studies have shown the potential of neural speech decoding, those are focused on speaker-dependent models. In this study, we investigated speaker-independent neural speech decoding of five continuous phrases from Magnetoencephalography (MEG) signals while 8 subjects produced speech covertly (imagination) or overtly (articulation). We have used both supervised and unsupervised speaker adaptation strategies for implementing a speaker independent model. Experimental results demonstrated that the proposed adaptation-based speakerindependent model has significantly improved decoding performance. To our knowledge, this is the first demonstration of the possibility of speaker-independent neural speech decoding.

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Time Domain Analysis of EEG to Classify Imagined Speech

Cited by 9 publications

References 6 publications

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Towards a Speaker Independent Speech-BCI Using Speaker Adaptation

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