The Effect of Auditory Stimuli on User’s Meditation and Workload in a Brain–Computer Interface Game

2019 ◽  
Vol 31 (3) ◽  
pp. 250-262 ◽  
Author(s):  
Gabriel Alves Mendes Vasiljevic ◽  
Leonardo Cunha de Miranda
Keyword(s):  
Brain Computer Interface ◽  
Controlled Experiment ◽  
The Self ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Subjective Workload ◽  
Brain Signals ◽  
Key Features ◽  
External Stimuli ◽  
Over Time

Abstract The advancement of Brain–computer interface (BCI) technology allowed for the development of applications not only for physically-impaired users, but for entertainment purposes as well. However, there are still numerous challenges in the development of such applications, as it is not known to which extent external stimuli may interfere with the captured brain signals. Being so, understanding the possible limitations caused by these external stimuli may help developers and designers in the development of future BCI-based applications. This paper presents the results of a controlled experiment designed for investigating the effects of auditory stimuli (AS) on subjects playing a neurofeedback-based game. The experiment consisted of sixteen volunteer subjects who played a total of twelve game matches each, for a total of 144 matches, over the course of three experiment sessions. Statistical analysis and qualitative instruments were employed to investigate key features of the subjects’ interaction with the game over time, especially regarding the influence of AS in both subjects’ performance and self-assessed, subjective workload. It was concluded that the subjects’ level of meditation tends to increase over time, that the self-assessed workload tends to decrease over time, and that the game’s AS did neither significantly influenced the performance nor the subjective workload of the subjects. Research Highlights The influence of auditory stimuli (AS) was investigated with a brain–computer interface game. Meditation level and workload were assessed and evaluated in a controlled experiment setup. Subjects’ performance tends to increase over time, while self-assessed workload tends to decrease. The presence of AS did not influenced the subjects’ meditation level and workload.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

2013 ◽  
Vol 310 ◽  
pp. 660-664 ◽  
Author(s):  
Zi Guang Li ◽  
Guo Zhong Liu
Keyword(s):  
Communication Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Support Vector ◽  
Quality Life ◽  
Asking Questions ◽  
Command Signals ◽  
Different Characteristics ◽  
The Brain

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

scholarly journals EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018 ◽  
Vol 8 (11) ◽  
pp. 199 ◽  
Author(s):  
Rodrigo Ramele ◽  
Ana Villar ◽  
Juan Santos
Keyword(s):  
Brain Computer Interface ◽  
Waveform Analysis ◽  
Computer Interface ◽  
Clinical Tool ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Public Dataset ◽  
Imaging Sensor ◽  
Clinical Eeg

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

scholarly journals A Survey of Interactive Systems based on Brain-Computer Interfaces

2013 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Alessandro Luiz Stamatto Ferreira ◽  
Leonardo Cunha de Miranda ◽  
Erica Esteves Cunha de Miranda ◽  
Sarah Gomes Sakamoto
Keyword(s):  
Virtual Reality ◽  
Brain Computer Interface ◽  
Research Area ◽  
Interactive Systems ◽  
Computer Interface ◽  
Immersive Environments ◽  
User Interactions ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Near Future

Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.

The Dynamic Boundaries of The Self: Serial Dependence In Embodied Sense of Agency

2021 ◽  
Author(s):  
Yoni Stern ◽  
Inbar Ben-Yehuda ◽  
Danny Koren ◽  
ADAM ZAIDEL ◽  
Roy Salomon
Keyword(s):  
The Self ◽  
Sense Of Agency ◽  
Embodied Experience ◽  
Serial Dependence ◽  
Actual Movement ◽  
Spatial Domains ◽  
Temporal And Spatial ◽  
Embodied Self ◽  
External Stimuli ◽  
Over Time

The feeling of control over one’s actions, termed the Sense of Agency (SoA), delineates one’s experience as an embodied self. Although, this embodied experience is typically perceived as stable over time, recent theoretical accounts highlight the experience-dependent and dynamic nature of the embodied self. In this study we examined how recent experiences modulate SoA (i.e., serial dependence), and disambiguated the unique contributions of previous stimuli and choices on subsequent SoA judgments. In addition, we examined whether these effects persist across different domains of perceptual alteration. We analyzed two independent datasets of the Virtual Hand (VH) task (N = 100 participants) in which a sensorimotor conflict is introduced between the presented visual feedback and the actual movement performed. In Dataset 1, which included only temporal alterations, we found that previous stimuli recalibrate current perception, increasing the likelihood of the current choice to be different than the previous choice. Whereas previous choices induce a repetition bias increasing the likelihood to repeat choices across trials. Thus, previous external stimuli and self-generated choices exert opposing influences on SoA. We replicated these findings in Dataset 2, in which the VH task was tested with alterations in both temporal and spatial domains. In addition, we discovered that previous stimuli from a different perceptual domain exert a recalibration effect similar to stimuli from the same domain. Thus, SoA is constantly shaped by our previous subjective choices and objective stimuli experienced even across different perceptual domains. This highlights how SoA may act as unifying construct organizing our experience of the self over time and across perceptual experiences.

scholarly journals The point of no return in vetoing self-initiated movements

2015 ◽  
Vol 113 (4) ◽  
pp. 1080-1085 ◽  
Author(s):  
Matthias Schultze-Kraft ◽  
Daniel Birman ◽  
Marco Rusconi ◽  
Carsten Allefeld ◽  
Kai Görgen ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Readiness Potential ◽  
Movement Onset ◽  
Brain Signals ◽  
Spontaneous Movements ◽  
Point Of No Return

In humans, spontaneous movements are often preceded by early brain signals. One such signal is the readiness potential (RP) that gradually arises within the last second preceding a movement. An important question is whether people are able to cancel movements after the elicitation of such RPs, and if so until which point in time. Here, subjects played a game where they tried to press a button to earn points in a challenge with a brain–computer interface (BCI) that had been trained to detect their RPs in real time and to emit stop signals. Our data suggest that subjects can still veto a movement even after the onset of the RP. Cancellation of movements was possible if stop signals occurred earlier than 200 ms before movement onset, thus constituting a point of no return.

scholarly journals The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Shih Chung Chen ◽  
Aaron Raymond See ◽  
Yeou Jiunn Chen ◽  
Chia Hong Yeng ◽  
Chih Kuo Liang
Keyword(s):  
Remote Control ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Response ◽  
Brain Signals ◽  
Information Transfer Rate ◽  
Cord Injury ◽  
Remote Controller ◽  
The Brain

People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ) module. A command selection training (CST) and simulated path test (SPT) were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.

scholarly journals A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

2021 ◽  
Vol 39 (7) ◽  
pp. 1117-1132
Author(s):  
Samaa S. Abdulwahab ◽  
Hussain K. Khleaf ◽  
Manal H. Jassim
Keyword(s):  
Brain Computer Interface ◽  
Physical World ◽  
The Body ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Rule Based ◽  
The Past ◽  
Brain Signals ◽  
Translation Systems ◽  
The Brain

A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally,  discussed is the application of EEG-based BCI.

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