Browsing by Author "Yasinzai, Muhammad Nabi"

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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.
Open Access
New approach for designing cVEP BCI stimuli based on superposition of edge responses
(Institute of Physics Publishing, 2020-06) Yasinzai, Muhammad Nabi; İder, Yusuf Ziya
The purpose of this study is to develop a new methodology for designing stimulus sequences for Brain Computer Interfaces that utilize code modulated Visually Evoked Potentials (cVEP BCIs), based on experimental results regarding the behavior and the properties of the actual EEG responses of the visual system to binary-coded visual stimuli, such that training time is reduced and possible number of targets is increased. EEG from 8 occipital sites is recorded with 2000 sps, in response to visual stimuli presented on a computer monitor with 60 Hz refresh rate. EEG responses of the visual system to black-to-white and white-to-black transitions of a target area on the monitor are recorded for 500 ms, for 160 trials, and signal-averaged to obtain the onset (positive edge) and offset (negative edge) responses, respectively. It is found that both edge responses are delayed by 50 ms and wane completely within 350 ms. These edge responses are then used to generate (predict) the EEG responses to arbitrary binary stimulus sequences using the superposition principle. It is found that the generated and the measured EEG responses to certain (16) simple short sequences (16.67–350 ms) are highly correlated. These 'optimal short patterns' are then randomly combined to design the long (120 bit, 2 sec) 'Superposition Optimized Pulse (SOP)' sequences, and their EEG response templates are obtained by superposition of the edge responses. A SOP sequence-based Visual Speller BCI application yielded higher accuracy (95.9%) and Information Transfer Rate (ITR) (57.2 bpm), compared to when superposition principle is applied to conventional m-sequences and randomly generated sequences. Training for the BCI application involves only the acquisition of the edge responses and takes less than 4 min. This is the first study in which the EEG templates for cVEP BCI sequences are obtained by the superposition of edge responses.