Nikhil Rathi

1. Electrocardiography (ECG) is a basic tool in contemporary cardiology, widely used for the diagnosis of heart diseases, routine screening, and continuous monitoring of patients [1–5]. This method...

The response of the P300-based speller is associated with factors like matrix size, inter-stimulus interval, and flashing period. This study proposes the comparison of the novel 2 × 2 image-based speller with the traditional 6 × 6 character-based speller to analyze the effects of the stimulus on the accuracy and information transfer rates. To determine the best classification methodology for the approach suggested, a comparative study was performed using linear and quadratic discrimination analysis, K-nearest neighbor, and support vector machine. In the proposed paradigm, four pictures (objects, special symbols, geometrical shapes, and colors) were randomly placed at four corners of the monitor. Subjects were asked to focus on the target image while ignoring all other images. The proposed method outperformed the traditional method, with an average accuracy of 96.99 ± 1.64% and 86.74 ± 5.19%, respectively, and information transfer rates of 33.82 ± 0.57 bits/min and 23.35 ± 0.79 bits/min, respectively. Results show that a modified speller can play a significant role in optimizing brain-computer interface-driven applications. A repeated measure ANOVA test was performed, which concluded that the improved results are obtained using QDA classifiers in terms of mean accuracy, speed, and error rates. Graphical abstract

Billion-dollar investment has been made globally in different industries, and hence, protection of organisational assets against theft or leakage of confidential information becomes a prime objective. Therefore, industries have to build their security system according to the job profile of workers. In this study, a pictorial hybrid brain–computer interface (hBCI) based 2 × 2 speller was proposed that combines P300 with steady-state-evoked potential (SSVEP) reactions to enhance the authentication system’s performance. Each run displays pictures of various objects at four corners of the screen where each location flickers and flashes simultaneously. Here, P300 and SSVEP stimulation are used in such a way that the position of the target picture is detected by SSVEP while the target picture is identified by P300 stimulus. The classification accuracies of hybrid and only P300 system are 97.63% and 94.61%, respectively, while the information transfer rates obtained were 70.34 and 30.26 bit/min, respectively.

With the recent advancement in the field of information technology, online frauds have also been increased and hence password-based systems are no more secure. Therefore need for a more reliable authentication system has been growing rapidly. This study focuses on a P300 based authentication system designing. The proposed study presents a new four-choice oddball paradigm where a conventional 6 × 6 character-based speller is replaced by a novel 2 × 2 pictorial speller. In this study, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Quadratic Support Vector Machine (QSVM). Out of these three, QDA performed better. The results showed that the designed visual stimuli obtained significantly higher accuracy (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The results also show that reduced matrix size and modified stimulus typically affects the accuracy and communication speed of a P300 based authentication system.