@article {4450,
	title = {Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.},
	journal = {J Neurol},
	volume = {270},
	year = {2023},
	month = {03/2023},
	pages = {1323-1336},
	abstract = {<p>Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease,~among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.</p>},
	keywords = {Amyotrophic Lateral Sclerosis, brain-computer interfaces, Electroencephalography, Humans, Self-Help Devices, Speech},
	issn = {1432-1459},
	doi = {10.1007/s00415-022-11464-6},
	author = {Vansteensel, Mariska J and Klein, Eran and van Thiel, Ghislaine and Gaytant, Michael and Simmons, Zachary and Wolpaw, Jonathan R and Vaughan, Theresa M}
}
@article {4362,
	title = {A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.},
	journal = {IEEE Trans Neural Syst Rehabil Eng},
	volume = {29},
	year = {2021},
	month = {2021},
	pages = {1766-1773},
	abstract = {<p>We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.</p>},
	keywords = {Algorithms, brain-computer interfaces, Electroencephalography, Evoked Potentials, Visual, Humans, Photic Stimulation},
	issn = {1558-0210},
	doi = {10.1109/TNSRE.2021.3106876},
	author = {Habibzadeh, Hadi and Norton, James J S and Vaughan, Theresa M and Soyata, Tolga and Zois, Daphney-Stavroula}
}
