Changes in emotional state modulate neuronal firing rates of human speech motor cortex: a case study in long-term recording

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier

Objective.Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels.Approach.In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity.Main results.Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively.Significance.Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.

Slow Firing Single Units Are Essential for Optimal Decoding of Silent Speech

The motivation of someone who is locked-in, that is, paralyzed and mute, is to find relief for their loss of function. The data presented in this report is part of an attempt to restore one of those lost functions, namely, speech. An essential feature of the development of a speech prosthesis is optimal decoding of patterns of recorded neural signals during silent or covert speech, that is, speaking “inside the head” with output that is inaudible due to the paralysis of the articulators. The aim of this paper is to illustrate the importance of both fast and slow single unit firings recorded from an individual with locked-in syndrome and from an intact participant speaking silently. Long duration electrodes were implanted in the motor speech cortex for up to 13 years in the locked-in participant. The data herein provide evidence that slow firing single units are essential for optimal decoding accuracy. Additional evidence indicates that slow firing single units can be conditioned in the locked-in participant 5 years after implantation, further supporting their role in decoding.

Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review

Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic in vivo environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. In-vivo diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.

Restoring cortical control of functional movement in a human with quadriplegia

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.

Examining the relationship between fatigue and cognition after stroke: A systematic review

Many stroke survivors experience fatigue, which is associated with a variety of factors including cognitive impairment. A few studies have examined the relationship between fatigue and cognition and have obtained conflicting results. The aim of the current study was to review the literature on the relationship between fatigue and cognition post-stroke. The following databases were searched: EMBASE (1980-February, 2014), PsycInfo (1806-February, 2014), CINAHL (1937-February, 2014), MEDLINE (1946-February, 2014), Ethos (1600-February, 2014) and DART (1999-February, 2014). Reference lists of relevant papers were screened and the citation indices of the included papers were searched using Web of Science. Studies were considered if they were on adult stroke patients and assessed the following: fatigue with quantitative measurements (≥ 3 response categories), cognition using objective measurements, and the relationship between fatigue and cognition. Overall, 413 papers were identified, of which 11 were included. Four studies found significant correlations between fatigue and memory, attention, speed of information processing and reading speed (r = -.36 to .46) whereas seven studies did not. Most studies had limitations; quality scores ranged from 9 to 14 on the Critical Appraisal Skills Programme Checklists. There was insufficient evidence to support or refute a relationship between fatigue and cognition post-stroke. More robust studies are needed.

Reducing the "Stress": Antioxidative Therapeutic and Material Approaches May Prevent Intracortical Microelectrode Failure

Despite the promising potential of intracortical microelectrodes, current designs suffer from short functional lifetimes, due in large part to the neuroinflammatory response to the implanted devices. An increasing body of literature is beginning to link neuroinflammatory-mediated oxidative damage to both the loss of neuronal structures around the implanted microelectrodes, and the degradation/corrosion of electrode materials. The goal of this viewpoint paper was to summarize the current progress toward understanding the role of oxidative damage to neurons and microelectrodes. Further, we seek to highlight the initial antioxidative approaches to mitigate oxidative damage, as well as suggest how current advances in macromolecular science for various applications may play a distinct role in enabling intracortical microelectrodes as reliable choices for long-term neuroprosthetic applications.

An integrated power, area and noise efficient AFE for large scale multichannel neural recording systems

A wideband, low-power, low-noise and area-efficient analog front-end (AFE) for acquiring neural signals is described. The AFE builds upon existing architectures but uses block-wise optimization to achieve superior performance when used in a multichannel system with scalable channel count. The AFE is also the first of its kind to enable acquisition from extended neural bandwidths greater than 10 kHz. The AFE is designed in 65 nm CMOS technology and consumes 11.3 μW of power while occupying 0.06 mm(2) per channel and delivering an NEF of 2.92.