scholarly journals An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 318
Author(s):  
Arrigo Palumbo ◽  
Nicola Ielpo ◽  
Barbara Calabrese
Keyword(s):  
High Performance ◽  
Visual Stimulation ◽  
Event Related Potentials ◽  
Individual Autonomy ◽  
Motor Disabilities ◽  
Eeg Data ◽  
Field Programmable ◽  
Severe Motor ◽  
Related Potentials ◽  
Bci System

Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.

scholarly journals Eliciting and Recording Event Related Potentials (ERPs) in Behaviourally Unresponsive Populations: A Retrospective Commentary on Critical Factors

2021 ◽  
Vol 11 (7) ◽  
pp. 835
Author(s):  
Alexander Rokos ◽  
Richard Mah ◽  
Rober Boshra ◽  
Amabilis Harrison ◽  
Tsee Leng Choy ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Event Related Potential ◽  
Critical Factors ◽  
Healthy Elderly ◽  
States Of Consciousness ◽  
Long Latency ◽  
Eeg Data ◽  
Related Potentials ◽  
The Relationship

A consistent limitation when designing event-related potential paradigms and interpreting results is a lack of consideration of the multivariate factors that affect their elicitation and detection in behaviorally unresponsive individuals. This paper provides a retrospective commentary on three factors that influence the presence and morphology of long-latency event-related potentials—the P3b and N400. We analyze event-related potentials derived from electroencephalographic (EEG) data collected from small groups of healthy youth and healthy elderly to illustrate the effect of paradigm strength and subject age; we analyze ERPs collected from an individual with severe traumatic brain injury to illustrate the effect of stimulus presentation speed. Based on these critical factors, we support that: (1) the strongest paradigms should be used to elicit event-related potentials in unresponsive populations; (2) interpretation of event-related potential results should account for participant age; and (3) speed of stimulus presentation should be slower in unresponsive individuals. The application of these practices when eliciting and recording event-related potentials in unresponsive individuals will help to minimize result interpretation ambiguity, increase confidence in conclusions, and advance the understanding of the relationship between long-latency event-related potentials and states of consciousness.

scholarly journals Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7198
Author(s):  
Juan David Chailloux Peguero ◽  
Omar Mendoza-Montoya ◽  
Javier M. Antelis
Keyword(s):  
Visual Stimulation ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Single Trial ◽  
Experimental Protocol ◽  
Training Time ◽  
Machine Learning Model ◽  
Visual Frame ◽  
Related Potentials ◽  
The Relationship

The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.

scholarly journals Electroencephalographic Correlates of Temporal Bayesian Belief Updating and Surprise

2020 ◽  
Author(s):  
Antonino Visalli ◽  
Mariagrazia Capizzi ◽  
Ettore Ambrosini ◽  
Bruno Kopp ◽  
Antonino Vallesi
Keyword(s):  
Event Related Potentials ◽  
Prior Beliefs ◽  
Belief Updating ◽  
Cognitive Domains ◽  
Bayesian Brain ◽  
Eeg Data ◽  
Electrode Space ◽  
Related Potentials ◽  
Brain Data ◽  
The Brain

The brain predicts the timing of forthcoming events to optimize responses to them. Temporal predictions have been formalized in terms of the hazard function, which integrates prior beliefs on the likely timing of stimulus occurrence with information conveyed by the passage of time. However, how the human brain updates prior temporal beliefs is still elusive. Here we investigated electroencephalographic (EEG) signatures associated with Bayes-optimal updating of temporal beliefs. Given that updating usually occurs in response to surprising events, we sought to disentangle EEG correlates of updating from those associated with surprise. Twenty-six participants performed a temporal foreperiod task, which comprised a subset of surprising events not eliciting updating. EEG data were analyzed through a regression-based massive approach in the electrode and source space. Distinct late positive, centro-parietally distributed, event-related potentials (ERPs) were associated with surprise and belief updating in the electrode space. While surprise modulated the commonly observed P3b, updating was associated with a later and more sustained P3b-like waveform deflection. Results from source analyses revealed that surprise encoding comprises neural activity in the cingulo-opercular network (CON). These data provide evidence that temporal predictions are computed in a Bayesian manner, and that this is reflected in P3 modulations, akin to other cognitive domains. Overall, our study revealed that analyzing P3 modulations provides an important window into the Bayesian brain. Data and scripts are shared on OSF: https://osf.io/ckqa5/?view_only=f711e6f878784d4ab94f4b14b31eef46

scholarly journals Developing a 3- to 6-state EEG-based brain-computer interface for a robotic manipulator control

2017 ◽  
Author(s):  
Yuriy Mishchenko ◽  
Murat Kaya ◽  
Erkan Ozbay ◽  
Hilmi Yanar
Keyword(s):  
Error Rate ◽  
High Performance ◽  
Processing System ◽  
Robotic Control ◽  
Signal Processing System ◽  
Eeg Data ◽  
Recent Developments ◽  
Assistive Systems ◽  
Time Required ◽  
Bci System

AbstractRecent developments in BCI techniques have demonstrated high-performance control of robotic prosthetic systems primarily via invasive methods. In this work we develop an electroencephalography (EEG) based noninvasive BCI system that can be used for a similar, albeit lower-speed robotic control, and a signal processing system for detecting user’s mental intent from EEG data based on up to 6-state motor-imagery BCI communication paradigm. We examine the performance of that system on experimental data collected from 12 healthy participants and analyzed offline. We show that our EEG BCI system can correctly identify different motor imageries in EEG data with high accuracy: 3 out of 12 participants achieved accuracy of 6-state communication in 80-90% range, while 2 participants could not achieve a satisfactory accuracy. We further implement an online BCI system for control of a virtual 3 degree-of-freedom prosthetic manipulator and test it with our 3 best participants. The participants’ ability to control the BCI is quantified by using the percentage of successfully completed BCI tasks, the time required to complete a task, and the error rate. 2 participants were able to successfully complete 100% of the test tasks, demonstrating on average the error rate of 80% and requiring 5-10 seconds to execute a manipulator move. 1 participant failed to demonstrate a satisfactory performance in online trials. Our results lay a foundation for further development of EEG BCI-based robotic assistive systems and demonstrate that EEG-based BCI may be feasible for robotic control by paralyzed and immobilized individuals.

scholarly journals Increasing the Mobility of EEG Data Collection Using a Latte Panda Computer

2018 ◽  
Author(s):  
Jonathan W. P. Kuziek ◽  
Eden X. Redman ◽  
Graeme D. Splinter ◽  
Kyle E. Mathewson
Keyword(s):  
Data Collection ◽  
Low Cost ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Laptop Computer ◽  
Muscle Movement ◽  
Auditory Oddball ◽  
Processing Power ◽  
Eeg Data ◽  
Related Potentials

AbstractBackgroundElectroencephalography (EEG) experiments often require several computers to ensure accurate stimulus presentation and data collection. However, this requirement can make it more difficult to perform such experiments in mobile settings within, or outside, the laboratoryNew MethodComputer miniaturisation and increasing processing power allow for EEG experiments to become more portable. Our goal is to show that a Latte Panda, a small Windows 10 computer, can be used to accurately collect EEG data in a similar manner to a laptop. Using a stationary bike, we also demonstrate that the Latte Panda will allow for more portable EEG experiments.ResultsSignificant and reliable MMN and P3 responses, event-related potentials (ERPs) typically associated with auditory oddball tasks, were observed and were consistent when using either the laptop or Latte Panda for EEG data collection. Similar MMN and P3 ERPs were also measured in the sitting and stationary biking conditions while using a Latte Panda for data collection.Comparison with Existing MethodData recorded by the Latte Panda computer produced comparable and equally reliable results to the laptop. As well, similar ERPs during sitting and biking would suggest that EEG experiments can be conducted in more mobile situations despite the increased noise and artefacts associated with muscle movement.ConclusionsOur results show that the Latte Panda is a low-cost, more portable alternative to a laptop computer for recording EEG data. Such a device will further allow for more portable and mobile EEG experimentation in a wider variety of environments.

scholarly journals Accuracy in Archery Shooting is linked to the Amplitude of the ERP N1 to the Snap of Clicker

2021 ◽  
Vol 10 (1) ◽  
pp. 37-44
Author(s):  
Hayri Ertan ◽  
◽  
Suha Yagcioglu ◽  
Alpaslan Yılmaz ◽  
Pekcan Ungan ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Event Related Potentials ◽  
Operational Definition ◽  
Brain Potentials ◽  
Long Latency ◽  
Eeg Data ◽  
Continuous Eeg ◽  
Related Potentials ◽  
High Level ◽  
The Brain

An archer requires a well-balanced and highly reproducible release of the bowstring to attain high scores in competition. Recurve archers use a mechanical device called the “clicker” to check the draw length. The fall of the clicker that generates an auditory stimulus should evoke a response in the brain. The purpose of this study is to evaluate the event-related potentials during archery shooting as a response to the fall of the clicker. Fifteen high-level archers participated. An electro cap was placed on the archers’ scalps, and continuous EEG activity was recorded (digitized at 1000 Hz) and stored for off-line analysis. The EEG data were epoched beginning 200 ms before and lasting 800 ms after stimulus marker signals. An operational definition has been developed for classifying hits corresponding to hit and/or miss areas. The hit area enlarged gradually starting from the centre of the target (yellow: 10) to blue (6 score) by creating ten hit area indexes. It is found that the snap of the clicker during archery shooting evokes N1–P2 components of long-latency evoked brain potentials. N1 amplitudes are significantly higher in hit area than that of miss areas for the 2nd and 4th indexes with 95% confidence intervals and 90% confidence intervals for the 1st and 3rd indexes with 90% confidence intervals. We conclude that the fall of the clicker in archery shooting elicits an N1 response with higher amplitude. Although evoked potential amplitudes were higher in successful shots, their latencies were not significantly different from the unsuccessful ones.

scholarly journals Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ying Mao ◽  
Jing Jin ◽  
Shurui Li ◽  
Yangyang Miao ◽  
Andrzej Cichocki
Keyword(s):  
Skin Friction ◽  
Information Transfer ◽  
High Performance ◽  
Brain Computer Interface ◽  
Event Related Potentials ◽  
Research Direction ◽  
Vital Role ◽  
Tactile Perception ◽  
Computer Interface ◽  
Related Potentials

Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped on target vibration motors, respectively. In both paradigms, the disturbance vibrators were wrapped in cotton. The experimental results showed that LSP could induce stronger event-related potentials (ERPs) and achieved a higher classification accuracy and information transfer rate (ITR) compared with SSP. The findings indicate that high skin friction can achieve high performance in tactile BCI. This work provides a novel research direction and constitutes a viable basis for the future tactile P300 BCI, which may benefit patients with visual impairments.

Effects of low-level visual information and perceptual load on P1 and N170 responses to emotional faces

2019 ◽  
Author(s):  
Sebastian Schindler ◽  
Maximilian Bruchmann ◽  
Bettina Gathmann ◽  
robert.moeck ◽  
thomas straube
Keyword(s):  
Visual Information ◽  
Perceptual Load ◽  
Visual Stimulation ◽  
Event Related Potentials ◽  
Visual Features ◽  
Early Event ◽  
Emotional Facial Expressions ◽  
Low Level ◽  
N170 Component ◽  
Related Potentials

Emotional facial expressions lead to modulations of early event-related potentials (ERPs). However, it has so far remained unclear in how far these modulations represent face-specific effects rather than differences in low-level visual features, and to which extent they depend on available processing resources. To examine these questions, we conducted two preregistered independent experiments (N = 40 in each experiment) using different variants of a novel task which manipulates peripheral perceptual load across levels but keeps overall visual stimulation constant. Centrally, task-irrelevant angry, neutral and happy faces and their Fourier phase-scrambled versions, which preserved low-level visual features, were presented. The results of both studies showed load-independent P1 and N170 emotion effects. Importantly, we could confirm by using Bayesian analyses that these emotion effects were face-independent for the P1 but not for the N170 component. We conclude that firstly, ERP modulations during the P1 interval strongly depend on low-level visual information, while the emotional N170 modulation requires the processing of figural facial features. Secondly, both P1 and N170 modulations appear to be immune to a large range of variations in perceptual load.

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