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Privacy and Ethics in Brain–Computer Interface Research
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Participation in BCI research involves the loss of some physical privacy. Placement of electroencephalography (EEG) electrodes onto the skull involves a loss of physical privacy in this regard, but the loss of physical privacy is more obviously the case with implantable BCI research where neurosurgeons must temporarily traverse or remove part of the skull in order to place electrodes into, atop, or in proximity to the brain. This is an intrusion, albeit voluntary, of the body and hence involves a loss of physical privacy. Even after surgery, physical privacy remains an issue insofar as an implanted device facilitates easier physical access to a person’s body. Participants in the BrainGate trial, for instance, have a pedestal attached to their skull that serves as an access port for attaching a data recording cable during BCI experiments (Hochberg et al. 2012). Facilitated physical access is not just a feature of the laboratory, though, but of the presence of a device and the bodily residuals of having been implanted with it. For instance, individuals with deep brain stimulators are subject to special screening at airports. Being pulled aside from standard metal detectors or other detection devices and showing one’s cranial scar or subcutaneous bulge of a battery placed under the chest wall as evidence of a pmed (personal medical electronic device) are forms of physical accessibility to which others are not subject.1 Individuals with implantable BCI devices are likely to face similar kinds of physical privacy loss.
Brain Implants
Published in L. Syd M Johnson, Karen S. Rommelfanger, The Routledge Handbook of Neuroethics, 2017
One of the most promising applications of BCIs has been the BrainGate 2 neural interface. This system can enable persons with amputated limbs or severe paralysis to control a prosthetic with their thoughts, mediated by the computer algorithm. The system uses electrodes implanted in the motor cortex to read signals related to limb movement. The algorithm decodes the signals and translates them, moving an external prosthetic such as a robotic arm or hand. In a study involving two volunteers with tetraplegia from brain-stem strokes, the system enabled one of the volunteers to grasp a foam ball with a prosthetic arm with a fairly high rate of success (Hochberg et al., 2012). This is one example of how a BCI can bypass the site of brain injury to restore some degree of motor control. Researchers can predict the movement the patient wants to perform, with the algorithm decoding EEG signals corresponding to the initial urge to perform that movement. But this raises the question of what controls the process of forming and translating the urge to move into the actual movement. This is not an issue in normal motor skills, which are performed unconsciously and automatically. Moving an artificial limb requires some degree of conscious deliberation. Yet if the movement is predictable on the basis of neural signals alone, then it seems that the subject and his conscious mental states do not initiate the movement. This suggests that restoration of physical control has everything to do with the BCI and the signals it decodes and nothing to do with the subject. The subject would have no causal role in the sequence of events resulting in the action, and the technique would not restore the loss of free will from the brain injury.
The future potential of the Stentrode
Published in Expert Review of Medical Devices, 2019
Sam E. John, David B. Grayden, Takufumi Yanagisawa
To date, the most impressive demonstrations of BMIs came from penetrating intracortical recordings from the BrainGate consortium, where some users could type up to 30 characters per minute by using thought to move a cursor on a screen to type letters [7]. While this is impressive, the technology faces several challenges in translation to clinical application. The StentrodeTM approach has so far shown reliable two-class classification in sheep [8] but continuous trajectory decoding is yet to be demonstrated. However, from a user’s point of view, functional control in a take-home BMI is appealing even if the speed of control is reduced. Only one BMI is presently in use at home by a person with paralysis, who uses a single command to select an object on a screen [9]. Even this single class classification system provides improvement to her everyday life. At the very least, the StentrodeTM is expected to be capable of separating movement intent or imagination versus rest. The first-in-human clinical trial (ClinicalTrials.gov Identifier: NCT03834857) using the StentrodeTM is scheduled for 2019. This trial is an Early Feasibility Study in participants with paralysis resulting from spinal cord injury, motor neuron disease, stroke, muscular dystrophy, or amputation.
Mapping the Dimensions of Agency
Published in AJOB Neuroscience, 2021
Andreas Schönau, Ishan Dasgupta, Timothy Brown, Erika Versalovic, Eran Klein, Sara Goering
A number of research projects aim to create assistive robotic devices for people with sensory and motor impairments. For example, the BRAINGATE device—an investigational system that drives a robotic arm using neural recordings—gave study participant Jan Scheuermann the ability to eat a chocolate bar on her own, despite the spinocerebellar degeneration that left her body paralyzed (Upson 2014). Earlier, we noted adjacent efforts aim to equip devices like these with sensory feedback (Cronin et al. 2016; Lee et al. 2018) for the sake of inducing feelings of ownership over them. Consider, then, the following fictional case based on these developments:
Dimensions of Ethical Direct-to-Consumer Neurotechnologies
Published in AJOB Neuroscience, 2019
Not too long ago, neurotechnology was the purview of the clinic and research. In 2011, researchers at Brown University succeeded for the first time in using an implanted sensor in the brain of a patient with tetraplegia to record neuronal activity and translating that activity into controlled movements of a robotic arm. They dubbed this new neurotechnological device ‘BrainGate’ (Hochberg et al. 2012). Funded in part by the military sector, such trials were designed to advance brain-computer interface (BCI) technology in order to restore a degree of autonomy to paralyzed and disabled individuals, such as wounded soldiers. Today, noninvasive versions of BCI devices are available directly to consumers, and while falling short of the functionalities of implanted BCIs, allow users to track their brain waves with electroencephalography (EEG) and to use brain activity to control their environments, such as by typing on keyboards, flying drones, or manipulating a panoply of smart objects (Emotiv 2018a). Similarly, invasive deep brain stimulation (DBS) technology was pioneered as a treatment for tremors in patients with Parkinson’s disease (Gardner 2013). Since then, noninvasive forms of brain stimulation, such as transcranial direct current stimulation (tDCS) and Vagus nerve stimulation, have been made available for consumers seeking to improve their cognitive abilities, enhance relaxation, or curb cravings (Langley 2017; Nervana 2018). Virtual reality (VR) systems that were once clunky, expensive, required precise positioning of computers and sensors throughout a room, and were mostly found in academic settings, have now shrunken to untethered headsets that are available to anyone with around $199 to spare (Oculus 2018). Similarly, while an individual used to have to visit a doctor’s office to receive an electroencephalogram (EEG) or an electrocardiogram (EKG), or purchase a purpose-built in-home device to measure her blood pressure, breathing volume, and blood-sugar, such measurements are now all directly available to a consumer through a plethora of commercially available wearable devices (Kreitmair and Cho 2017). Finally, while psychotherapy used to require traveling to a provider’s office and spending a set amount of time in a face-to-face session, consumers today have access to over 10,000 smartphone mental health apps that seek to fill the same or similar purposes as traditional therapy (Martinez-Martin and Kreitmair 2018; Torous et al. 2019).