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Cortical stimulation through electrocorticographic (ECoG) electrodes is a potential method for providing sensory feedback in future prosthetic and rehabilitative applications. Here, we evaluate human subjects’ ability to continuously modulate their motor behavior based on feedback from direct surface stimulation of the somatosensory cortex. Subjects wore a dataglove that measured their hand aperture position and received one of three stimuli over the hand sensory cortex based on their current hand position as compared to a target aperture position. Using cortical stimulation feedback, subjects adjusted their hand aperture to move towards the target aperture region. One subject was able to achieve accuracies and R2 values well above chance (best performance: R2 = 0.93; accuracy = 0.76/1). Performance dropped during the catch trial (same stimulus independent of the position) to below chance levels, suggesting that the subject had been using the varied sensory feedback to modulate their motor behavior. To our knowledge, this study represents one of the first demonstrations of using direct cortical surface stimulation of the human sensory cortex to perform a motor task, and is a first step towards developing closed-loop human sensorimotor brain-computer interfaces.

Goal: To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions. Methods: This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13–14, 2014. Results: The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies. Conclusions: The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology. Significance: The development of new neurotechnologies (e.g., bidirectional brain–computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.

Probabilistic methods for analyzing sequences are providing new insights into the 4,000-year-old undeciphered script of the Indus civilization.

Electrocorticographic spectral changes during movement show a behavioral inflection in the classic gamma band (30-70 Hz). We quantify this inflection and demonstrate that it limits classification accuracy. We call for the designation of a functionally defined band above it, which we denote the chi-band.

Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.

Recently, CNN reported on the future of brain-computer interfaces (BCIs). BCIs are devices that process a user's brain signals to allow direct communication and interaction with the environment. BCIs bypass the normal neuromuscular output pathways and rely on digital signal processing and machine learning to translate brain signals to action (Figure 1). Historically, BCIs were developed with biomedical applications in mind, such as restoring communication in completely paralyzed individuals and replacing lost motor function. More recent applications have targeted nondisabled individuals by exploring the use of BCIs as a novel input device for entertainment and gaming. The task of the BCI is to identify and predict behaviorally induced changes or "cognitive states" in a user's brain signals. Brain signals are recorded either noninvasively from electrodes placed on the scalp [electroencephalogram (EEG)] or invasively from electrodes placed on the surface of or inside the brain. BCIs based on these recording techniques have allowed healthy and disabled individuals to control a variety of devices. In this article, we will describe different challenges and proposed solutions for noninvasive brain-computer interfacing.