scholarly journals Noise-based cyberattacks generating fake P300 waves in brain–computer interfaces

2021 ◽  
Author(s):  
Enrique Tomás Martínez Beltrán ◽  
Mario Quiles Pérez ◽  
Sergio López Bernal ◽  
Alberto Huertas Celdrán ◽  
Gregorio Martínez Pérez
Keyword(s):  
At Risk ◽  
Acquisition Phase ◽  
Brain Computer Interfaces ◽  
Proper Functioning ◽  
Computer Interfaces ◽  
Electroencephalographic Signals ◽  
The Impact

AbstractMost of the current Brain–Computer Interfaces (BCIs) application scenarios use electroencephalographic signals (EEG) containing the subject’s information. It means that if EEG were maliciously manipulated, the proper functioning of BCI frameworks could be at risk. Unfortunately, it happens in frameworks sensitive to noise-based cyberattacks, and more efforts are needed to measure the impact of these attacks. This work presents and analyzes the impact of four noise-based cyberattacks attempting to generate fake P300 waves in two different phases of a BCI framework. A set of experiments show that the greater the attacker’s knowledge regarding the P300 waves, processes, and data of the BCI framework, the higher the attack impact. In this sense, the attacker with less knowledge impacts 1% in the acquisition phase and 4% in the processing phase, while the attacker with the most knowledge impacts 22% and 74%, respectively.

scholarly journals Investigating the impact of feedback update interval on the efficacy of restorative brain–computer interfaces

2017 ◽  
Vol 4 (8) ◽  
pp. 170660 ◽  
Author(s):  
Sam Darvishi ◽  
Michael C. Ridding ◽  
Brenton Hordacre ◽  
Derek Abbott ◽  
Mathias Baumert
Keyword(s):  
Sensory Feedback ◽  
Motor Evoked Potentials ◽  
Single Case ◽  
Maximum Voluntary Contraction ◽  
Voluntary Contraction ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Timely Fashion ◽  
Clinically Significant ◽  
The Impact

Restorative brain–computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored. There is evidence that sensory feedback disinhibits the motor cortex. Thus, in this study, we explore how shorter than usual FUIs affect behavioural and neurophysiological measures following BCI training for stroke patients using a single-case proof-of-principle study design. The action research arm test was used as the primary behavioural measure and showed a clinically significant increase (36%) over the course of training. The neurophysiological measures including motor evoked potentials and maximum voluntary contraction showed distinctive changes in early and late phases of BCI training. Thus, this preliminary study may pave the way for running larger studies to further investigate the effect of FUI magnitude on the efficacy of restorative BCIs. It may also elucidate the role of early and late phases of motor learning along the course of BCI training.

An Analysis of the Impact of Brain-Computer Interfaces on Autonomy

Neuroethics ◽  
2018 ◽  
Author(s):  
Orsolya Friedrich ◽  
Eric Racine ◽  
Steffen Steinert ◽  
Johannes Pömsl ◽  
Ralf J. Jox
Keyword(s):  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
The Impact

scholarly journals Breaching Subjects’ Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mario Quiles Pérez ◽  
Enrique Tomás Martínez Beltrán ◽  
Sergio López Bernal ◽  
Alberto Huertas Celdrán ◽  
Gregorio Martínez Pérez
Keyword(s):  
Evoked Potentials ◽  
Visual Stimuli ◽  
System Response ◽  
Sensitive Information ◽  
Brain Computer Interfaces ◽  
Sensitive Data ◽  
Computer Interfaces ◽  
Clinical Scenarios ◽  
Important Challenge ◽  
The Impact

Brain-computer interfaces (BCIs) started being used in clinical scenarios, reaching nowadays new fields such as entertainment or learning. Using BCIs, neuronal activity can be monitored for various purposes, with the study of the central nervous system response to certain stimuli being one of them, being the case of evoked potentials. However, due to the sensitivity of these data, the transmissions must be protected, with blockchain being an interesting approach to ensure the integrity of the data. This work focuses on the visual sense, and its relationship with the P300 evoked potential, where several open challenges related to the privacy of subjects’ information and thoughts appear when using BCI. The first and most important challenge is whether it would be possible to extract sensitive information from evoked potentials. This aspect becomes even more challenging and dangerous if the stimuli are generated when the subject is not aware or conscious that they have occurred. There is an important gap in this regard in the literature, with only one work existing dealing with subliminal stimuli and BCI and having an unclear methodology and experiment setup. As a contribution of this paper, a series of experiments, five in total, have been created to study the impact of visual stimuli on the brain tangibly. These experiments have been applied to a heterogeneous group of ten subjects. The experiments show familiar visual stimuli and gradually reduce the sampling time of known images, from supraliminal to subliminal. The study showed that supraliminal visual stimuli produced P300 potentials about 50% of the time on average across all subjects. Reducing the sample time between images degraded the attack, while the impact of subliminal stimuli was not confirmed. Additionally, younger subjects generally presented a shorter response latency. This work corroborates that subjects’ sensitive data can be extracted using visual stimuli and P300.

Trends and Prospects of Using Brain-Computer Interfaces in Education

2021 ◽  
pp. 108-129
Author(s):  
D.S. Gnedykh ◽  
Keyword(s):  
Information Acquisition ◽  
Cognitive Activity ◽  
Educational Process ◽  
Self Control ◽  
Learning Activity ◽  
Brain Computer Interfaces ◽  
Activity Intensity ◽  
Computer Interfaces ◽  
First Case ◽  
The Impact

The relevance of the brain-computer interfaces (BCI) implementation in the field of edu-cation is conditioned by the realization of long-life and individualized learning concepts, as well as the requirement of effective and affordable automated learning systems. The article presents the analysis of studies on BCI usage in the educational process, in order to systema-tize the evidence, identify emerging trends and determine the difficulties and prospects of their applications in education. Nowadays, two main directions of BCI application for the purpose of training quality im-provement are revealed. In the first direction, the researchers’ attention is focused on psycho-physiology, meaning the identification of student’s current state characteristics and its timely correction with the teacher’s help (or self-correction). The second one emphasizes the peda-gogical aspect of BCI usage, such as monitoring the student’s cognitive activity in the process of course content perception to determine the most optimal parameters and conditions of its presentation. In the first case, the change of a student’s state or activity is emphasized, in the second one the changes relate to correction of learning content and its delivery. Among the main difficulties of using BCI in education are the following: problems with the equipment of modern BCI systems, the lack of clear classifications of neurophysiological correlates of various mental phenomena, the difficulty of consideration and differentiation of all the factors affecting a user during his interaction with BCI in natural environment. The prospects of BCI usage in learning are proposed: 1. Prediction of learning activity productivity; 2. Development of students’ self-control in the educational process; 3. Real-time identification of cognitive and affective students’ states in learning certain subjects (mathematics, physics, computer science, etc.); 4. Assessment of the impact of electronic learning tools on the process of information acquisition; 5. Monitoring the dynamics of cognitive activity intensity in students while solving dif-ferent learning tasks; 6. Identification of the available amount of information for its successful processing at the neurophysiological level to optimize the delivery of learning materials.

scholarly journals Being Outside the Decision-Loop: The Impact of Deep Brain Stimulation and Brain-Computer Interfaces on Autonomy

2021 ◽  
Vol 56 ◽  
pp. 25-52
Author(s):  
Monika Michałowska ◽  
Łukasz Kowalczyk ◽  
Weronika Marcinkowska ◽  
Mikołaj Malicki
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
The Impact ◽  
Deep Brain

scholarly journals Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

2021 ◽  
Vol 15 ◽  
Author(s):  
Nikki Leeuwis ◽  
Alissa Paas ◽  
Maryam Alimardani
Keyword(s):  
Motor Imagery ◽  
Visual Imagery ◽  
Personality Factors ◽  
Classification Error ◽  
Imagery Task ◽  
Brain Computer Interfaces ◽  
Classification Error Rate ◽  
Psychological Measures ◽  
Computer Interfaces ◽  
The Impact

Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users’ mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

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