A cybersecure P300-based brain-to-computer interface against noise-based and fake P300 cyberattacks

In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions

Analysis of consciousness for complete locked-in syndrome patients

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL)

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy