Prospects of sustainable ADHD treatment through Brain-Computer Interface systems

Zafar, Maryam Binte; Shah, Khadija Akbar; Malik, Hassan Adnan

doi:10.1109/icieect.2017.7916532

Cited by 14 publications

(5 citation statements)

References 32 publications

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“…BCI applications may fall in the medical arena or the consumer arena. Within the medical arena, BCIs are often intended to restore or increase communication and motor skills for persons with physical impairments, improve rehabilitation for persons who have had a stroke or spinal cord injury, and help to regulate or treat persons with psychiatric conditions or epilepsy [9][10][11][12][13]. The consumer arena consists of BCI applications such as gaming, entertainment and enhancement [14,15].…”

Section: Introductionmentioning

confidence: 99%

What is it like to use a BCI? – insights from an interview study with brain-computer interface users

Kögel¹,

Jox

Friedrich

2020

BMC Med Ethics

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Background: The neurotechnology behind brain-computer interfaces (BCIs) raises various ethical questions. The ethical literature has pinpointed several issues concerning safety, autonomy, responsibility and accountability, psychosocial identity, consent, privacy and data security. This study aims to assess BCI users' experiences, selfobservations and attitudes in their own right and looks for social and ethical implications. Methods: We conducted nine semi-structured interviews with BCI users, who used the technology for medical reasons. The transcribed interviews were analyzed according to the Grounded Theory coding method. Results: BCI users perceive themselves as active operators of a technology that offers them social participation and impacts their self-definition. Each of these aspects bears its own opportunities and risks. BCIs can contribute to retaining or regaining human capabilities. At the same time, BCI use contains elements that challenge common experiences, for example when the technology is in conflict with the affective side of BCI users. The potential benefits of BCIs are regarded as outweighing the risks in that BCI use is considered to promote valuable qualities and capabilities. BCI users appreciate the opportunity to regain lost capabilities as well as to gain new ones. Conclusions: BCI users appreciate the technology for various reasons. The technology is highly appreciated in cases where it is beneficial in terms of agency, participation and self-definitions. Rather than questioning human nature, the technology can retain and restore characteristics and abilities which enrich our lives.

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Section: Introductionmentioning

confidence: 99%

What is it like to use a BCI? – insights from an interview study with brain-computer interface users

Kögel¹,

Jox

Friedrich

2020

BMC Med Ethics

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“…It has been demonstrated that AR lengthens and increases the frequency of imaginative play. [32] . Teaching New Tasks: AR interventions can help people with ADHD in studying and nishing new tasks.…”

Section: Ar Implication On Adhdmentioning

confidence: 99%

Exploring the Potential of Augmented Reality Games for Managing Autism and ADHD: A Promising Alternative Approach

Joseph,

Vinay,

Warrier

2023

Preprint

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Attention-Deficit/Hyperactivity Disorder ADHD significantly impairs a person's ability to function on a daily basis and negatively impacts their general wellbeing. Alternative methods are becoming more popular, despite the fact that standard therapies like medicine and behavioral therapy are frequently used. The usage of augmented reality (AR) games, Artificial Intelligence (AI) and neurofeedback therapy are the new possibilities we study and this article focuses on importance of AR. With its interactive and immersive features, augmented reality (AR) has potential for controlling ADHD, especially in pediatric populations. AR games can be particularly created to treat issues connected to ADHD and improve executive functioning, focus, and attention. These gamification techniques offer creative and entertaining solutions that assist people with ADHD in managing symptoms and enhancing overall functioning by utilizing the captivating qualities of AR.

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“…There was a study that showed that 90% of children were able to improve against their ADHD symptoms, which were maintained even after a 6-month follow-up using neurofeedback training [ 3 ]. Past research suggested that one can perform better in sports activities after they undergo implicit motor learning on movement, albeit only slightly using biofeedback [ 4 , 5 ]. The goal of this research is to provide means to obtain data to improve implicit learning skills by introducing a concept of neurofeedback for learning events for healthy adults using the Smart Education concept.…”

Section: Introductionmentioning

confidence: 99%

“…To be a better implicit learner using neurofeedback, there has to be a way to identify when the implicit learning event occurs so that we can train individuals’ brains to be better at implicit learning. The pattern of Implicit Learning events will be identified as learners go through learning activities to achieve this goal [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

Chaiyanan

Iramina

Kaewkamnerdpong

2021

Entropy

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The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence.

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Prospects of sustainable ADHD treatment through Brain-Computer Interface systems

Cited by 14 publications

References 32 publications

What is it like to use a BCI? – insights from an interview study with brain-computer interface users

What is it like to use a BCI? – insights from an interview study with brain-computer interface users

Exploring the Potential of Augmented Reality Games for Managing Autism and ADHD: A Promising Alternative Approach

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

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