A Motivational Model of BCI-Controlled Heuristic Search

Cavazza, Marc

doi:10.3390/brainsci8090166

Cited by 7 publications

(5 citation statements)

References 62 publications

(117 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, visually realistic feedback with a social component (such as in our RAP dataset) has been shown to foster good NF responses even with minimal training (Mathiak et al, 2015). Finally, more speculative explanations could involve improved reward encoding with realistic visual feedback, in some cases even resonating with reward encoding in the RoI itself (Cavazza, 2018), in particular in the case of DLPFC (Tanaka et al, 2006;Aupperle et al, 2015).…”

Section: Neurofeedback Conceptsmentioning

confidence: 86%

Prefrontal Asymmetry BCI Neurofeedback Datasets

2020

Self Cite

View full text Add to dashboard Cite

Prefrontal cortex (PFC) asymmetry is an important marker in affective neuroscience and has attracted significant interest, having been associated with studies of motivation, eating behavior, empathy, risk propensity, and clinical depression. The data presented in this paper are the result of three different experiments using PFC asymmetry neurofeedback (NF) as a Brain-Computer Interface (BCI) paradigm, rather than a therapeutic mechanism aiming at long-term effects, using functional near-infrared spectroscopy (fNIRS) which is known to be particularly well-suited to the study of PFC asymmetry and is less sensitive to artifacts. From an experimental perspective the BCI context brings more emphasis on individual subjects' baselines, successful and sustained activation during epochs, and minimal training. The subject pool is also drawn from the general population, with less bias toward specific behavioral patterns, and no inclusion of any patient data. We accompany our datasets with a detailed description of data formats, experiment and protocol designs, as well as analysis of the individualized metrics for definitions of success scores based on baseline thresholds as well as reference tasks. The work presented in this paper is the result of several experiments in the domain of BCI where participants are interacting with continuous visual feedback following a real-time NF paradigm, arising from our long-standing research in the field of affective computing. We offer the community access to our fNIRS datasets from these experiments. We specifically provide data drawn from our empirical studies in the field of affective interactions with computer-generated narratives as well as interfacing with algorithms, such as heuristic search, which all provide a mechanism to improve the ability of the participants to engage in active BCI due to their realistic visual feedback. Beyond providing details of the methodologies used where participants received real-time NF of left-asymmetric increase in activation in their dorsolateral prefrontal cortex (DLPFC), we re-establish the need for carefully designing protocols to ensure the benefits of NF paradigm in BCI are enhanced by the ability of the real-time visual feedback to adapt to the individual responses of the participants. Individualized feedback is paramount to the success of NF in BCIs.

show abstract

Section: Neurofeedback Conceptsmentioning

confidence: 86%

Prefrontal Asymmetry BCI Neurofeedback Datasets

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, we envision future developments of similar studies aiming at cognitive neuro-augmentation. Such studies would then serve as platforms integrating behavioral and brain data with artificial intelligence and machine learning algorithms to better understand decision-making, by calibrating biases and thus optimizing behavior ([ 68 , 69 ]).…”

Section: Discussionmentioning

confidence: 99%

Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market

Toma¹,

Miyakoshi

2021

Brain Sciences

View full text Add to dashboard Cite

Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1–4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments.

show abstract

“…On the other hand, a head-controlled mouse uses sensors such as accelerometers and gyroscopes to detect head actions or captures information about head movements through computer vision [6]. Brainwave recognition, as a developing technology [7,8] allows people with disabilities to use their minds to control mouse movements. Furthermore, some other popular mouse assistive tools use mouth control technologies, which include the control of a sip and puff switch [9], bite operation [10], and mouth shape recognition [11].…”

Section: Introductionmentioning

confidence: 99%

A Computer Mouse Using Blowing Sensors Intended for People with Disabilities

Chen

Huang

Tsai

et al. 2019

Sensors

View full text Add to dashboard Cite

The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer mouse, but they all have some operational inconveniences. In this paper, we propose an innovative blowing-controlled mouse assistive tool to replace the conventional hand-controlled mouse. Its main contribution is that it uses microphones to induce small signals through the principle of airflow vibration, and it then converts the received signal into the corresponding pulse width. The co-design of software programming enables various mouse functions to be implemented by the identification of the blowing pulse width of multiple microphones. The proposed tool is evaluated experimentally, and the experimental results show that the average identification rate of the proposed mouse is over 85%. Additionally, compared with the other mouse assistive tools, the proposed mouse has the benefits of low cost and humanized operation. Therefore, the proposed blowing control method can not only improve the life quality of people with disabilities but also overcome the disadvantages of existing assistive tools.

show abstract

A Motivational Model of BCI-Controlled Heuristic Search

Cited by 7 publications

References 62 publications

Prefrontal Asymmetry BCI Neurofeedback Datasets

Prefrontal Asymmetry BCI Neurofeedback Datasets

Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market

A Computer Mouse Using Blowing Sensors Intended for People with Disabilities

Contact Info

Product

Resources

About