The potential of the BCI for accessible and smart e-learning

The brain computer interface (BCI) should be the accessibility solution “par excellence” for interactive and e-learning systems. There is a substantial tradition of research on the human electro encephalogram (EEG) and on BCI systems that are based, inter alia, on EEG measurement. We have not yet seen a viable BCI for e-learning. For many users for a BCI based interface is their first choice for good quality interaction, such as those with major psychomotor or cognitive impairments. However, there are many more for whom the BCI would be an attractive option given an acceptable learning overhead, including less severe disabilities and safety critical conditions where cognitive overload or limited responses are likely. Recent progress has been modest as there are many technical and accessibility problems to overcome. We present these issues and report a survey of fifty papers to capture the state-of-the-art in BCI and the implications for e-learning

Assistive technologies : short overview and trends

This paper gives a brief overview of currently existing assistive technologies for different kinds of disabilities. An elaborate discussion of all types of assistive technologies is beyond the scope of this paper. Assistive technologies have evolved dramatically in recent years and will continue to be further developed thanks to major progress in artificial intelligence, machine learning, robotics, and other areas. Previously, assistive technologies were highly specialized and were often difficult or expensive to acquire. Today, however, many assistive technologies are included in mainstream products and services. An introduction and state of the art of assistive technologies are presented first. These are followed by an overview of technological trends in assistive technologies and a conclusion

BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project

This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants. Results: The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of “ideal types” of disabled users may reinforce stereotypes or drown out participant “voices”. Conclusions: Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a “duty of care” while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies

Inclusive Intelligent Learning Management System Framework

Machado, D. S-M., & Santos, V. (2023). Inclusive Intelligent Learning Management System Framework. International Journal of Automation and Smart Technology, 13(1), [2423]. https://doi.org/10.5875/ausmt.v13i1.2423The article finds context and the current state of the art in a systematic literature review on intelligent systems employing PRISMA Methodology which is complemented with narrative literature review on disabilities, digital accessibility and legal and standards context. The main conclusion from this review was the existing gap between the available knowledge, standards, and law and what is put into practice in higher education institutions in Portugal. Design Science Research Methodology was applied to output an Inclusive Intelligent Learning Management System Framework aiming to help higher education professors to share accessible pedagogic content and deliver on-line and presential classes with a high level of accessibility for students with different types of disabilities, assessing the uploaded content with Web content Accessibility Guidelines 3.0, clustering students according to their profile, conscient feedback and emotional assessment during content consumption, applying predictive models and signaling students at risk of failing classes according to study habits and finally applying a recommender system. The framework was validated by a focus group to which experts in digital accessibility, information systems and a disabled PhD graduate.publishersversionpublishe

Classifying motor imagery in presence of speech

In the near future, brain-computer interface (BCI) applications for non-disabled users will require multimodal interaction and tolerance to dynamic environment. However, this conflicts with the highly sensitive recording techniques used for BCIs, such as electroencephalography (EEG). Advanced machine learning and signal processing techniques are required to decorrelate desired brain signals from the rest. This paper proposes a signal processing pipeline and two classification methods suitable for multiclass EEG analysis. The methods were tested in an experiment on separating left/right hand imagery in presence/absence of speech. The analyses showed that the presence of speech during motor imagery did not affect the classification accuracy significantly and regardless of the presence of speech, the proposed methods were able to separate left and right hand imagery with an accuracy of 60%. The best overall accuracy achieved for the 5-class separation of all the tasks was 47% and both proposed methods performed equally well. In addition, the analysis of event-related spectral power changes revealed characteristics related to motor imagery and speech

A Review on Brain-Controlled Home Automation

A "smart home" employs ambient intelligence to keep tabs on things around the house so that the owner may get services tailored to their specific needs and control their home appliances from afar. Home automation for the elderly and handicapped focuses on enabling older persons and those with disabilities to live safely and comfortably at home. Additionally, the integration of this technology with a brain-computer interface (BCI) is perhaps of tremendous usefulness to those who are either old or disabled. These BCI-based brain-controlled home automation (BCHA) systems have emerged as a viable option for people with neuro disorders to remain in their homes rather than move to assisted living facilities. To summarize, BCI-based BCHA for the elderly and handicapped people is transforming people's lives every day. Most individuals prefer a simple approach to save time and effort. Automating the house is the simplest way for individuals to save time and effort. The brain-computer interface, often known as a BCI, is an innovative method of human-computer connection that does not rely on conventional output channels (muscle tissue and peripheral nerve). Over the course of the last three decades, it has attracted the attention of industry experts and developed into a thriving centre for research. Brain-controlled home automation (BCHA), as a typical BCI application, may provide physically challenged people with a new communication route with the outside world. However, the primary challenge that BCHA faces is to rapidly decipher multi-degree-of-freedom control instructions extracted from an electroencephalogram (EEG). The BCHA's research has made significant headway in a short amount of time during the last fifteen years. This study investigates the BCHA from several viewpoints, including the pattern of instructions for the control system, the type of signal acquisition, and the operational mechanism of the control system itself. This paper a concise description of the building blocks of smart homes and how they may be used to construct BCI-controlled home automation to assist disabled individuals. It is a compilation of information pertaining to communication protocols, multimedia devices, sensors, and systems that are often used in the process of putting smart homes into action. A comprehensive strategy for developing a functional and sustainable BCI-controlled home automation system is laid out in this paper as well, which could be useful to researchers in the future