Browsing by Subject "Brain-computer interface (BCI)"

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Open Access
Hybrid and model based approaches for new BCI spellers
(2019-07) Memon, Suleman Aijaz
Electroencephalography (EEG) based brain-computer interfaces (BCIs), due to their non-invasive, portable and temporal resolution properties, are widely used in the field of neural engineering. In order to make BCI paradigms more practical and feasible for real life applications, new approaches are being tested such as hybrid BCIs and model based BCIs. In the first phase of this study, a novel hybrid speller BCI is proposed, incorporating P300 and code-modulated visual evoked potential (c-VEP) paradigms, with the objective of improving the spelling accuracy and information transfer rate (ITR), compared to individual P300 and c-VEP paradigms. Moreover, fusion techniques have been applied in order to effectively combine the information of P300 and c-VEP at the score level. We have implemented and compared two different approaches, linear discriminant analysis (LDA) and maximum probability estimation (MPE), in order to identify which one works best for this hybrid BCI. The proposed BCI consists of 36 targets presented as 6x6 matrix on screen with a refresh rate of 120 Hz. Seven healthy subjects participated in experiments where each subject performed a training session followed by five test sessions. The P300 and c-VEP signals are obtained by using bandpass filters of 0.5-6 Hz and 6-41 Hz respectively, on the raw hybrid data. For P300, stepwise linear discriminant analysis (SWLDA) is performed on training data from all the 10 EEG channels to obtain the feature vector. For c-VEP, canonical correlation analysis (CCA) is performed on training data to obtain the reference templates for all 36 symbols. In comparison with the accuracy and ITR values of c-VEP alone, that is without simultaneously making use of the P300 data obtained during the hybrid experiments, MPE-based hybrid improved only by 1.1% and 2.1 bits/min, on average, respectively, whereas the values worsened by 12.3% and 19.8 bits/min in the case of LDA-based hybrid. Moreover, the statistical tests on the mean accuracy and ITR values of all the subjects showed that the results of MPE-based hybrid and of c-VEP alone are not statistically different (p=0.293). Although the MPE-based hybrid is not statistically better than the c-VEP alone, it can be highly effective if the primary goal is to only increase the accuracy, using a range of improvements in P300 methods as discussed in conclusion. However, it would not be useful if the purpose is to increase the speed of the speller since the individual c-VEP paradigm, when optimized for timing, has the capability of giving an average ITR of 114.9bits/min or higher, on its own. In the second phase of this study, model based c-VEP BCI is implemented, aimed at improving the training time compared to the case where all the targets are assigned arbitrary pseudorandom binary sequences and training is required for all the symbols separately. For this purpose, moving average model has been implemented to simulate the responses for c-VEP visual stimulation patterns, for 60Hz and 120Hz monitor refresh rate respectively. The average of the correlation between measured response and modeled response for 60Hz and 120Hz is 0.357 and 0.396 respectively. The average accuracy and ITR obtained for model based c-VEP BCI is 87.1% and 76.4 bits/min for 60Hz respectively and 82.1% and 72.4 bits/min for 120Hz respectively. Modeling results suggest that it is possible to perform a training on a single visual stimulus pattern and achieve a good fit model.
Open Access
New approach for designing cVEP BCI stimuli based on superposition of edge responses
(2020-01) Yasinzai, Muhammad Nabi
Electroencephalography (EEG) based brain-computer interfaces (BCIs) are widely used in the ﬁeld of neural engineering, due to their portability, noninvasive nature, and high temporal resolution properties. Among the diﬀerent BCI modalities, code modulated visual evoked potentials (cVEP) are very popular due to their high classiﬁcation speed and accuracy. Over the years, various cVEP stimulus sequences have been designed aiming to increase the classiﬁcation speed, accuracy, and the number of supported targets. This study is carried out in order to present a novel cVEP stimulus sequence designing methodology, which is purely based on characteristics of the actual brain responses to visual stimuli. Seven male subjects participated in our study, and they were presented pulsetype visual stimulus sequences on a monitor with 60 Hz refresh rate (each bit of a stimulus sequence is presented for 16.67 ms). EEG was recorded using Brain Products V-Amp (16 channel) EEG Ampliﬁer from O1, Oz, O2, P3, Pz, P4, P7, and P8 positions at the rate of 2000 sps and the recorded EEG was then bandpass ﬁltered between 4 and 40 Hz. Electrode impedances were kept under 10 KOhms, and Canonical Correlation Analysis (CCA) was used to reduce the 8-channel data to a single signal. Matlab, along with psychtoolbox, was used for stimulus presentation on a PC with Ubuntu operating system. In the ﬁrst part of this study, our aim was to reconstruct the EEG response to pulse-type stimulus patterns by superposing the EEG responses to simple stimulus patterns. It is observed that the EEG response is only sensitive to the changes in the stimulus sequence, that is, to positive (change from Black to White) and negative edges (change from White to Black). The edge responses have a delay of around 50 ms, and these responses can be observed up to 350 ms after the edge. Furthermore, the magnitude of the positive edge response is much larger than the negative edge response. Edge responses for every person are unique, and they are also repeatable. The 7 subjects of our experimental study have an overall average correlation of 84% between the positive edge responses obtained with two weeks of separation. It is also interesting to know that edge responses for all of the subjects have a similar overall pattern. A series of experiments are then carried out to determine how well the EEG responses can be predicted by superposition of the edge responses. The reconstructed and measured EEG responses are compared for diﬀerent pulse widths, diﬀerent pulse separations, and also for diﬀerent pulse repetitions. It is observed that response to 1 and 2 bit wide pulses can be predicted accurately for all subjects with an average correlation of 70.3% and 68.1%, respectively. Further, for 1 bit wide pulses, if the separation between two pulses is 4 to 9 bits, the correlation between predicted and measured responses is above 51.5%. For 2 bit wide pulses, if the separation is between 3 and 9 bits, the correlation is above 53.4%. Furthermore, responses to repeating 2 and 3 bit wide pulses can be predicted with a correlation of up to 62.4% and 59.2% for 4 and 5 repetitions, respectively. In the third part of this study, we constructed 120 bit stimulus sequences based on the constraints explained above and compared them with two other types of stimulus sequences in the context of a BCI speller application. The proposed BCI speller consists of 36 targets that are presented as a 6x6 matrix on the monitor screen at the refresh rate of 60 Hz, and the experiments are performed on seven healthy subjects. The classiﬁcation results of our BCI speller follow our expectations based on the second part of our study; in that, for our proposed BCI stimulus sequences, the accuracy and ITR are recorded to be 95.5% and 57.19 bits/min, respectively, whereas for the other two types of codes, the classiﬁcation accuracies are 6.94% and 10.53% with information transfer rates (ITR) of 1.7 bits/min and 10.53 bits/min, respectively.