Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface

Brain-computer interfaces (BCIs), a new type of rehabilitation technology, pick up nerve cell signals, identify and classify their activities, and convert them into computer-recognized instructions. This technique has been widely used in the rehabilitation of stroke patients in recent years and appears to promote motor function recovery after stroke. At present, the application of BCI in poststroke cognitive impairment is increasing, which is a common complication that also affects the rehabilitation process. This paper reviews the promise and potential drawbacks of using BCI to treat poststroke cognitive impairment, providing a solid theoretical basis for the application of BCI in this area.

Treating Electrical and Biopotential Artifacts in an EEG Pilot Study Experiment

With the increase in life expectancy, as well as in the performance and complexity of healthcare systems, the need for fast and accurate information has also grown. EEG devices have become more accessible and necessary in clinical practice. In daily activity, artifacts are ubiquitous in EEG signals. They arise from: environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. This paper proposes an artifact cleaning pipeline including filters and algorithms to streamline the analysis process. Moreover, to better characterize and discriminate artifacts from useful EEG data, additional physiological signals and video data are used, which are correlated with subject's behavior. We quantify the performance reached by Peak Signal-to-Noise Ratio and clinical visual inspection. The entire research and data collection took place in the laboratories of XPERI Corporation.Clinical Relevance-Since the occurrence of artifacts cannot be controlled, it is essential to have a precise process of recognition, identification and elimination of noise. Therefore, it is important to distinguish EEG artifacts from abnormal activity in order to minimize the chance of EEG misinterpretation, that can lead to false diagnosis, especially regarding the study of epileptiform activities or other neurologic or psychiatric disorders (e.g. degenerative diseases, dementia, depression, sleep disorders, Alzheimer's disease, schizophrenia, etc.).

Emotion Recognition Positively Correlates with Steady-state Visual Evoked Potential Amplitude and Alpha Entrainment

Emotion recognition reflects the psychological and physiological status of humans. Numerous studies have investigated the neural mechanisms of emotion recognition based on electroencephalography (EEG) features. In the previous study, emotion target was presented under a static or irregular background, which made the response highly time-locked. As an oscillatory component of EEG, steady-state visual evoked potential (SSVEP) has distinctive frequency and phase properties, which provides more stable information than the other components of EEG. This study combined the emotion target with SSVEP to explore neural mechanisms of visual neurons under flickering background. Three basic emotions (delightfulness, sadness and, anger) were presented in 216 frequency-intensity conditions. Participants were asked to recognize the emotions and make judgments. The degree of alpha entrainment (valued as normalized Shannon entropy), SSVEP amplitude and recognition accuracy were calculated as response features. The results indicated that: SSVEP amplitude and recognition accuracy positively correlated with each other in frequency domain (7-15 Hz); alpha entrainment, and recognition accuracy had similar linear variation in intensity domain (level 1-4), and had a threshold around intensity 3; the three basic emotions had no clear relationship with each other in recognition. This study provided a new sight for neuroscience and would be an important reference to clinical psychology.

Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces

The time between the onset of subsequent auditory or visual stimuli - also known as stimulus onset asynchrony (SOA) - determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value influences the performance of an individual subject in brain-computer interface (BCI) applications like spellers. In the past, subject-specific optimization of the SOA was rarely considered in BCI studies. Our research strives to reduce the time requirements of individual BCI stimulus parameter optimization. This work contributes to this goal in two ways. First, we show that even the classification performance on extremely reduced training data subsets reveals the influence of SOA. Second, we show, that these noisy estimates are sufficient to make decisions for individual choices of the SOA that transfer to good classification performance on large training data sets. Thus our work contributes to individually tailored SOA selection procedures for BCI users.

Decoding Movement From Electrocorticographic Activity: A Review

Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems : Guidelines Derived from Simulation and Real-World Data

We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.

Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics

Brain computer interfaces (BCIs) have been applied to sensorimotor systems for many years. However, BCI technology has broad potential beyond sensorimotor systems. The emerging field of cognitive prosthetics, for example, promises to improve learning and memory for patients with cognitive impairment. Unfortunately, our understanding of the neural mechanisms underlying these cognitive processes remains limited in part due to the extensive individual variability in neural coding and circuit function. As a consequence, the development of methods to ascertain optimal control signals for cognitive decoding and restoration remains an active area of inquiry. To advance the field, robust tools are required to quantify time-varying and task-dependent brain states predictive of cognitive performance. Here, we suggest that network science is a natural language in which to formulate and apply such tools. In support of our argument, we offer a simple demonstration of the feasibility of a network approach to BCI control signals, which we refer to as network BCI (nBCI). Finally, in a single subject example, we show that nBCI can reliably predict online cognitive performance and is superior to certain common spectral approaches currently used in BCIs. Our review of the literature and preliminary findings support the notion that nBCI could provide a powerful approach for future applications in cognitive prosthetics.