An embedded implementation based on adaptive filter bank for brain–computer interface systems

Belwafi, Kais; Romain, Olivier; Gannouni, Sofien; Ghaffari, Fakhreddine; Djemal, Ridha; Ouni, Bouraoui

doi:10.1016/j.jneumeth.2018.04.013

Cited by 67 publications

(53 citation statements)

References 39 publications

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“…With an increased interest in mobile brain computer interfaces [ 12 , 13 , 14 ], engineers and researchers have sought front-ends which are low cost, have a small form factor, and possess good electrical characteristics [ 15 ]. Consequently, in the past two years alone, they have appeared in six novel BCIs, namely, an interactive care system for aged patients with dementia [ 16 ], a modular hybrid BCI based on EEG and near infra-red spectroscopy (NIRS) [ 17 ], a plug and play BCI for active and assisted living control [ 18 ], a hybrid BCI combining P300 and auditory steady state response (ASSR) [ 19 ], an embedded BCI for classification of event related synchronisation/desynchronisation [ 20 ], and a study of stimuli design for a BCI based on steady state evoked potentials (SSVEP) [ 21 ]. However, these BCIs have been tested in only a small number of participants and evaluations of the signal quality have relied on comparisons to past literature.…”

Section: Introductionmentioning

confidence: 99%

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Rashid

Niazi

Signal

et al. 2018

Sensors

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Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

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Section: Introductionmentioning

confidence: 99%

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Rashid

Niazi

Signal

et al. 2018

Sensors

View full text Add to dashboard Cite

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“…During brain activity, while moving one of the human's limbs, the shape of SMRs changes continuously with time. Evoked SMRs appear and vary continuously in specific frequency bands such as (α, [8][9][10][11][12] Hz), (β, Hz) and (γ, Hz) [7], [8]. Furthermore, these rhythms appear at a specific location of the brain lobes, depending on the limbs moved.…”

Section: Figure 1: Typical Eeg Signal Processing Chainmentioning

confidence: 99%

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

2020

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Brain-computer interface (BCI) acquires, analyzes and transforms human brain activity to control commands allowing as such disabled people to communicate or control external devices. A motor imagery-based BCI enables patients to control artificial peripherals and communicate with the outside world by merely thinking of the task such as, e.g., the imagination of left-hand, right-hand, or foot movement. The mere intention of moving one of the limbs triggers neural activity, which is induced in the primary sensorimotor areas like that observed with real executed movements. Tracking generated sensorimotor rhythms (SMRs) and extracting robust and informative features from electroencephalogram (EEG) signals are challenging due to the time-varying nature of EEG signals and the inter-human variability. In this paper, we proposed an EEG-zeros-time windowing (E2ZTW) approach based on a highly decaying window function to track SMRs and identify the temporal epochs containing useful information without any prior information on the trigger. The proposed approach involves the application of the group-delay function, allowing the improvement of the spectral resolution due to the additive property of the function on individual resonances. Some algorithms were integrated into the proposed approach, such as the common spatial pattern algorithm, which is used to extract features and linear discriminant analysis and a convolutional neural network, which are used for the classification of the features. The effectiveness of the proposed approach in tracking the SMRs rhythms is evaluated in terms of accuracy. Experiments were performed on three public datasets provided by BCI competition for 17 subjects. Following experimental results, it is shown that discrimination between the left-and right-hand movements can be achieved within a few seconds with high classification accuracy. As compared to other state-of-art techniques, the proposed approach achieves an average classification accuracy and standard error values of 82% and 13, respectively, thereby outperforming existing algorithm by an accuracy mean of 2%.

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“…MI has been used widely in BCI. By imagining or performing a muscle movement action, the power of mu (8)(9)(10)(11)(12)(13) and beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31) rhythms in the sensorimotor cortex will decrease or increase. Researchers hypothesized that an appropriate pattern of this phenomenon can be used as a suitable feature/signature in the classification.…”

Section: Introductionmentioning

confidence: 99%

“…For BCI, one of the most commonly used features is the common spatial pattern (CSP) [9][10][11][12][13][14][15]. In [9], CSP was combined with some features such as variance, entropy, energy and logarithmic band power (LBP) and then, LDA, SVM and artificial neural network (ANN) were used for classification of hand and foot movement.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network based features for motor imagery EEG signals classification in brain–computer interface system

2020

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One of the essential challenges in brain-computer interface is to classify motor imagery (MI) signals. In this paper, an ensemble SVM-based voting system is proposed. In each line of this system, the EEG signal is transformed into different representations based on discrete cosine transform, Fourier transform, common spatial pattern, and empirical mode decomposition, and then these representations are combined in a triple-frame matrix. These frames are fed into a pre-trained deep convolutional neural network as a feature extractor. For each line, an SVM is employed to classify the extracted feature vectors. Finally, a decision is made based on voting between these SVMs. Performance of the proposed method is examined on the BCI Competition III dataset Iva to separate right hand and foot movement imagery. The simple proposed method achieves the average accuracy of 96.34% for all of the subjects, and 99.70% for the best situation that is an improvement in MI classification. In addition, it can be seen that right side of the brain is more effective than the other side in EEG-based MI classification.

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An embedded implementation based on adaptive filter bank for brain–computer interface systems

Cited by 67 publications

References 39 publications

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

An Effective Zeros-Time Windowing Strategy to Detect Sensorimotor Rhythms Related to Motor Imagery EEG Signals

Convolutional neural network based features for motor imagery EEG signals classification in brain–computer interface system

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