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AI-driven brain-computed interfaces aimed at restoring speech for individuals living with locked-in-syndrome are paired with ethical implications for user's autonomy, privacy and responsibility. Embedding options for sufficient levels of user-control in speech-BCI design has been proposed to mitigate these ethical challenges. However, how user-control in speech-BCIs is conceptualized and how it relates to these ethical challenges is underdetermined. In this narrative literature review, we aim to clarify and explicate the notion of user-control in speech-BCIs, to better understand in what way user-control could operationalize user's autonomy, privacy and responsibility and explore how such suggestions for increasing user-control can be translated to recommendations for the design or use of speech-BCIs. First, we identified types of user control, including executory control that can protect voluntariness of speech, and guidance control that can contribute to semantic accuracy. Second, we identified potential causes for a loss of user-control, including contributions of predictive language models, a lack of ability for neural control, or signal interference and external control. Such a loss of user control may have implications for semantic accuracy and mental privacy. Third we explored ways to design for user-control. While embedding initiation signals for users may increase executory control, they may conflict with other aims such as speed and continuity of speech. Design mechanisms for guidance control remain largely conceptual, similar trade-offs in design may be expected. We argue that preceding these tradeoffs, the overarching aim of speech-BCIs needs to be defined, requiring input from current and potential users. Additionally, conceptual clarification of usercontrol and other (ethical) concepts in this debate has practical relevance for BCI researchers. For instance, different concepts of inner speech may have distinct ethical implications. Increased clarity of such concepts can improve anticipation of ethical implications of speech-BCIs and may help to steer design decisions. [ABSTRACT FROM AUTHOR]

Recent scientific achievements bring the concept of neural prosthetics for reinstating lost motor function closer to medical application. Current research involves severely paralyzed people under the age of 65, but implications for seniors with stroke or trauma-induced impairments are clearly on the horizon. Demographic changes will lead to a shortage of personnel to care for an increasing population of senior citizens, threatening maintenance of an acceptable level of care and urging ways for people to live longer at their home independent from personal assistance. This is particularly challenging when people suffer from disabilities such as partial paralysis after stroke or trauma, where daily personal assistance is required. For some of these people, neural prosthetics can reinstate some lost motor function and/or lost communication, thereby increasing independence and possibly quality of life. In this viewpoint article, we present the state of the art in decoding brain activity in the service of brain-computer interfacing. Although some noninvasive applications produce good results, we focus on brain implants that benefit from better quality brain signals. Fully implantable neural prostheses for home use are not available yet, but clinical trials are being prepared. More sophisticated systems are expected to follow in the years to come, with capabilities of interest for less severe paralysis. Eventually the combination of smart robotics and brain implants is expected to enable people to interact well enough with their environment to live an independent life in spite of motor disabilities. [ABSTRACT FROM AUTHOR]

Objective. The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex. Approach. Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex. Main results. Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score. Significance. The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke., (© 2021 IOP Publishing Ltd.)

For people who cannot communicate due to severe paralysis or involuntary movements, technology that decodes intended speech from the brain may offer an alternative means of communication. If decoding proves to be feasible, intracranial Brain-Computer Interface systems can be developed which are designed to translate decoded speech into computer generated speech or to instructions for controlling assistive devices. Recent advances suggest that such decoding may be feasible from sensorimotor cortex, but it is not clear how this challenge can be approached best. One approach is to identify and discriminate elements of spoken language, such as phonemes. We investigated feasibility of decoding four spoken phonemes from the sensorimotor face area, using electrocorticographic signals obtained with high-density electrode grids. Several decoding algorithms including spatiotemporal matched filters, spatial matched filters and support vector machines were compared. Phonemes could be classified correctly at a level of over 75% with spatiotemporal matched filters. Support Vector machine analysis reached a similar level, but spatial matched filters yielded significantly lower scores. The most informative electrodes were clustered along the central sulcus. Highest scores were achieved from time windows centered around voice onset time, but a 500 ms window before onset time could also be classified significantly. The results suggest that phoneme production involves a sequence of robust and reproducible activity patterns on the cortical surface. Importantly, decoding requires inclusion of temporal information to capture the rapid shifts of robust patterns associated with articulator muscle group contraction during production of a phoneme. The high classification scores are likely to be enabled by the use of high density grids, and by the use of discrete phonemes. Implications for use in Brain-Computer Interfaces are discussed., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: Electrocortical stimulation mapping (ESM) is the current gold standard for functional mapping of the eloquent cortex prior to epilepsy surgery. The procedure is, however, time-consuming and quite demanding for patients. Electrocorticography frequency mapping (ECoG mapping) has been suggested as an adjunct method. Here, we investigated whether it is possible to perform mapping of motor regions using ECoG data of spontaneous movements., Methods: Using the video registration of seven epilepsy patients who underwent electrocorticography and ESM, we selected periods of spontaneous hand and arm movements and periods of rest. Frequency analysis was performed, and electrodes showing a significant change in power (4-7, 8-14, 15-25, 26-45 or 65-95 Hz) were compared with those being identified as relevant for hand and/or arm movement by ESM., Results: All frequency bands showed a high specificity (>0.80), and the 65-95 Hz frequency band additionally had a high sensitivity (0.82) for identifying ESM positive electrodes., Conclusions: Our data show a good match between ECoG mapping of spontaneous movements and ESM data., Significance: The accurate match suggests that ECoG mapping of the motor cortex using spontaneous movements may be a valuable complement to ESM, especially when other options requiring patient cooperation fail., (Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)