A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential

Mohebbi, Ali; Engelsholm, Signe K.D.; Puthusserypady, Sadasivan; Kjær, Troels W.; Thomsen, Carsten; Sørensen, Helge Bjarup Dissing

doi:10.1109/embc.2015.7318434

Cited by 9 publications

(22 citation statements)

References 9 publications

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“…The most common domain where BCI c-VEPs are employed is the matrix spellers. It has been previously shown that they outperform the traditional BCIs regarding ITR performance (Mohebbi et al, 2015 ; Riechmann et al, 2016 ) with classification accuracies also being comparable.…”

Section: Introductionmentioning

confidence: 86%

Enhancing Performance and Bit Rates in a Brain–Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs

Dimitriadis

Marimpis

2018

Front. Neuroinform.

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A brain–computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class (N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class (N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class (N = 5) flickering BCI system. Estimating cross-frequency coupling (CFC) and namely δ-θ [δ: (0.5–4 Hz), θ: (4–8 Hz)] phase-to-amplitude coupling (PAC) within sensor and across experimental time, we succeeded in achieving high classification accuracy and Information Transfer Rates (ITR) in the three data sets. Our approach outperformed the originally presented ITR on the three data sets. The bit rates obtained for both the disabled and able-bodied subjects reached the fastest reported level of 324 bits/min with the PAC estimator. Additionally, our approach outperformed alternative signal features such as the relative power (29.73 bits/min) and raw time series analysis (24.93 bits/min) and also the original reported bit rates of 10–25 bits/min. In the second data set, we succeeded in achieving an average ITR of 124.40 ± 11.68 for the slow 60 Hz and an average ITR of 233.99 ± 15.75 for the fast 120 Hz. In the third data set, we succeeded in achieving an average ITR of 106.44 ± 8.94. Current methodology outperforms any previous methodologies applied to each of the three free available BCI datasets.

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Section: Introductionmentioning

confidence: 86%

Enhancing Performance and Bit Rates in a Brain–Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs

Dimitriadis

Marimpis

2018

Front. Neuroinform.

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“…For each test subject and each code, primary ( T p ) and secondary ( T s ) thresholds were calculated for use in the classification algorithm. The primary threshold ( T p ) was defined as ( 1 ): whereby R denotes the Pearson’s product-moment correlation coefficient given in ( 2 ), y ij is the j th average response to the i th target, denotes the average response to the i th target, n t is the number of targets, n r is the number of repetitions recorded for each target, and α = 0.8 is a thresholding constant based on the work in [ 19 ], included to accept some variation in the responses. whereby and N is the number of samples in the vectors X and Y .…”

Section: Methodsmentioning

confidence: 99%

“…The secondary threshold ( T s ) was defined as ( 3 ): whereby β = 0.625 is a scaling factor to lessen the thresholding criteria. The value was found empirically in [ 19 ] and used in this study as well.…”

Section: Methodsmentioning

confidence: 99%

“…A gold-code is based on m-sequences and additionally contains good cross-correlation properties [ 21 ]. The gold-code has also previously been used in BCI research and its relation to the m-sequence warrants its inclusion [ 19 , 22 ]. The Barker-code, although not often employed for BCI, is a promising pseudorandom sequence for its great ACF properties [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

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Optimal pseudorandom sequence selection for online c-VEP based BCI control applications

2017

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BackgroundIn a c-VEP BCI setting, test subjects can have highly varying performances when different pseudorandom sequences are applied as stimulus, and ideally, multiple codes should be supported. On the other hand, repeating the experiment with many different pseudorandom sequences is a laborious process.AimsThis study aimed to suggest an efficient method for choosing the optimal stimulus sequence based on a fast test and simple measures to increase the performance and minimize the time consumption for research trials.MethodsA total of 21 healthy subjects were included in an online wheelchair control task and completed the same task using stimuli based on the m-code, the gold-code, and the Barker-code. Correct/incorrect identification and time consumption were obtained for each identification. Subject-specific templates were characterized and used in a forward-step first-order model to predict the chance of completion and accuracy score.ResultsNo specific pseudorandom sequence showed superior accuracy on the group basis. When isolating the individual performances with the highest accuracy, time consumption per identification was not significantly increased. The Accuracy Score aids in predicting what pseudorandom sequence will lead to the best performance using only the templates. The Accuracy Score was higher when the template resembled a delta function the most and when repeated templates were consistent. For completion prediction, only the shape of the template was a significant predictor.ConclusionsThe simple and fast method presented in this study as the Accuracy Score, allows c-VEP based BCI systems to support multiple pseudorandom sequences without increase in trial length. This allows for more personalized BCI systems with better performance to be tested without increased costs.

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“…spelling device) [6,7], interaction with external devices (e.g. controlling a wheelchair) [8,9], rehabilitation [10,11] and/or for monitoring the mental states [12,13].…”

Section: Introductionmentioning

confidence: 99%

Real-time brain computer interface using imaginary movements

El-Madani

Sørensen

Kjær

et al. 2015

EPJ Nonlinear Biomed Phys

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Background: Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper, the implementation of a 2-class real-time BCI system based on the event related desynchronization (ERD) of the sensorimotor rhythms (SMR) is described. Methods: Off-line measurements were conducted on 12 healthy test subjects with 3 different feedback systems (cross, basket and bars). From the collected electroencephalogram (EEG) data, the optimum frequency bands for each of the subjects were determined first through an exhaustive search on 325 bandpass filters. The features were then extracted for the left and right hand imaginary movements using the Common Spatial Pattern (CSP) method. Subsequently, a Bayes linear classifier (BLC) was developed and used for signal classification. These three subject-specific settings were preserved for the on-line experiments with the same feedback systems. Results: Six of the 12 subjects were qualified for the on-line experiments based on their high off-line classification accuracies (CAs > 75 %). The overall mean on-line accuracy was found to be 80%. Conclusions:The subject-specific settings applied on the feedback systems have resulted in the development of a successful real-time BCI system with high accuracies.

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A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential

Cited by 9 publications

References 9 publications

Enhancing Performance and Bit Rates in a Brain–Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs

Enhancing Performance and Bit Rates in a Brain–Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs

Optimal pseudorandom sequence selection for online c-VEP based BCI control applications

Real-time brain computer interface using imaginary movements

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