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The Ethics of Brain-Computer Interfaces

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The development of computational systems known as brain-computer interfaces (BCIs) offers the possibility of allowing individuals disabled by neurological disorders such as Amyotrophic Lateral Sclerosis (ALS) and ischemic stroke the

The development of computational systems known as brain-computer interfaces (BCIs) offers the possibility of allowing individuals disabled by neurological disorders such as Amyotrophic Lateral Sclerosis (ALS) and ischemic stroke the ability to perform relatively complex tasks such as communicating with others and walking. BCIs are closed-loop systems that record physiological signals from the brain and translate those signals into commands that control an external device such as a wheelchair or a robotic exoskeleton. Despite the potential for BCIs to vastly improve the lives of almost one billion people, one question arises: Just because we can use brain-computer interfaces, should we? The human brain is an embodiment of the mind, which is largely seen to determine a person's identity, so a number of ethical and philosophical concerns emerge over current and future uses of BCIs. These concerns include privacy, informed consent, autonomy, identity, enhancement, and justice. In this thesis, I focus on three of these issues: privacy, informed consent, and autonomy. The ultimate purpose of brain-computer interfaces is to provide patients with a greater degree of autonomy; thus, many of the ethical issues associated with BCIs are intertwined with autonomy. Currently, brain-computer interfaces exist mainly in the domain of medicine and medical research, but recently companies have started commercializing BCIs and providing them at affordable prices. These consumer-grade BCIs are primarily for non-medical purposes, and so they are beyond the scope of medicine. As BCIs become more widespread in the near future, it is crucial for interdisciplinary teams of ethicists, philosophers, engineers, and physicians to collaborate to address these ethical concerns now before BCIs become more commonplace.

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Date Created
  • 2016-05

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Optimizing Biofeedback and Learning in an EEG-Based Brain-Computer Interface

Description

Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate

Brain-computer interface technology establishes communication between the brain and a computer, allowing users to control devices, machines, or virtual objects using their thoughts. This study investigates optimal conditions to facilitate learning to operate this interface. It compares two biofeedback methods, which dictate the relationship between brain activity and the movement of a virtual ball in a target-hitting task. Preliminary results indicate that a method in which the position of the virtual object directly relates to the amplitude of brain signals is most conducive to success. In addition, this research explores learning in the context of neural signals during training with a BCI task. Specifically, it investigates whether subjects can adapt to parameters of the interface without guidance. This experiment prompts subjects to modulate brain signals spectrally, spatially, and temporally, as well differentially to discriminate between two different targets. However, subjects are not given knowledge regarding these desired changes, nor are they given instruction on how to move the virtual ball. Preliminary analysis of signal trends suggests that some successful participants are able to adapt brain wave activity in certain pre-specified locations and frequency bands over time in order to achieve control. Future studies will further explore these phenomena, and future BCI projects will be advised by these methods, which will give insight into the creation of more intuitive and reliable BCI technology.

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Date Created
  • 2014-05