Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.

Objective. Brain-computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.Approach. Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments.Main results. Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by naïve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech.Significance.As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.

Seizure Prediction: Science Fiction or Soon to Become Reality?

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.

Feasibility of Energy-Autonomous Wireless Microsensors for Biomedical Applications: Powering and Communication.

In this review, biomedical-related wireless miniature devices such as implantable medical devices, neural prostheses, embedded neural systems, and body area network systems are investigated and categorized. The two main subsystems of such designs, the RF subsystem and the energy source subsystem, are studied in detail. Different application classes are considered separately, focusing on their specific data rate and size characteristics. Also, the energy consumption of state-of-the-art communication practices is compared to the energy that can be generated by current energy scavenging devices, highlighting gaps and opportunities. The RF subsystem is classified, and the suitable architecture for each category of applications is highlighted. Finally, a new figure of merit suitable for wireless biomedical applications is introduced to measure the performance of these devices and assist the designer in selecting the proper system for the required application. This figure of merit can effectively fill the gap of a much required method for comparing different techniques in simulation stage before a final design is chosen for implementation.

Ultra-low-power wireless transmitter for neural prostheses with modified pulse position modulation.

An ultra-low-power wireless transmitter for embedded bionic systems is proposed, which achieves 40 pJ/b energy efficiency and delivers 500 kb/s data using the medical implant communication service frequency band (402-405 MHz). It consumes a measured peak power of 200 µW from a 1.2 V supply while occupying an active area of 0.0016 mm(2) in a 130 nm technology. A modified pulse position modulation technique called saturated amplified signal is proposed and implemented, which can reduce the overall and per bit transferred power consumption of the transmitter while reducing the complexity of the transmitter architectures, and hence potentially shrinking the size of the implemented circuitry. The design is capable of being fully integrated on single-chip solutions for surgically implanted bionic systems, wearable devices and neural embedded systems.