When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation

The goal of this study is to investigate the influence of mental fatigue on the event related potential P300 features (maximum pick, minimum amplitude, latency and period) during virtual wheelchair navigation. For this purpose, an experimental environment was set up based on customizable environmental parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between P300 and fatigue ratings was conducted. Finally, the best correlated features supplied three classification algorithms which are MLP (Multi Layer Perceptron), Linear Discriminate Analysis and Support Vector Machine. The results showed that the maximum feature over visual and temporal regions as well as period feature over frontal, fronto-central and visual regions were correlated with mental fatigue levels. In the other hand, minimum amplitude and latency features didn’t show any correlation. Among classification techniques, MLP showed the best performance although the differences between classification techniques are minimal. Those findings can help us in order to design suitable mental fatigue based wheelchair control.     

Influence of Stimuli Spatial Proximity on a SSVEP-Based BCI Performance

Objective: Steady-State Visual Evoked Potentials based Brain-Computer Interfaces (SSVEP-based BCIs) systems have been shown as promising technology due to their short response time and ease of use. SSVEP-based BCIs use brain responses to a flickering visual stimulus as an input command to an external application or device, and it can be influenced by stimulus properties, signal recording, and signal processing. We aim to investigate the system performance varying the stimuli spatial proximity (a stimulus property).Material and methods: We performed a comparative analysis of two visual interface designs (named cross and square) for an SSVEP-based BCI. The power spectrum density (PSD) was used as feature extraction and the Support Machine Vector (SVM) as classification method. We also analyzed the effects of five flickering frequencies (6.67, 8.57, 10, 12 e 15 Hz) between and within interfaces.Results: We found higher accuracy rates for the flickering frequencies of 10, 12, and 15 Hz. The stimulus of 10 Hz presented the highest SSVEP amplitude response for both interfaces. The system presented the best performance (highest classification accuracy and information transfer rate) using the cross interface (lower visual angle).Conclusion: Our findings suggest that the system has the highest performance in the spatial proximity range from 4° to 13° (visual angle). In addition, we conclude that as the stimulus spatial proximity increases, the interference from other stimuli reduces, and the SSVEP amplitude response decreases, which reduces system accuracy. The inter-stimulus distance is a visual interface parameter that must be chosen carefully to increase the efficiency of an SSVEP-based BCI.     

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., <1 cm/<1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning¹, auditory processing² and visual-spatial attention³. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function^1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user''s intentions to enhance or improve communication⁶ and control⁷. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform^8,9 and the SIGFRIED¹⁰ method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.

Prerequisites and Planning:

Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient''s brain, and on relevant factors of the patient''s history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient''s performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital''s institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient''s endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient''s eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5^th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software^8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers'' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient''s electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping¹⁰.The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.     

Analysis of User Control Attainment in SMR-based Brain Computer Interfaces

Context

Sensorimotor rhythms (SMR) have been the neuronal phenomena of choice in non-invasive EEG-based endogenous brain computer interfaces (BCIs) for more than two decades and SMR-based BCIs have achieved the highest degree of freedom control so far. Nevertheless, they are subject to long periods of training prior to attaining a satisfactory level of control requiring users to learn to modulate their rhythms. The goal of this work is to analyse this problem, discuss the causes of the slow rise in performance and provide recommendations on alternative solutions to quicken control attainment.

脑机接口专利情报研究

脑机接口是在无外周神经系统和肌肉组织参与的条件下,通过计算机等电子设备输出控制信号,进而与外界环境进行交流的全新通讯和控制技术。它的发展依赖于神经科学、心理学、工程学、康复医学和计算机等学科专家间的密切合作,具有非常重要的学术价值、科学意义和广阔的应用前景,是当今世界研究的热点。本文利用新一代专利分析平台和工具Innography,结合专利情报分析理论,在对世界范围内脑机接口技术的专利进行申请趋势分析、区域分析、IPC分析、专利权人分析、诉讼专利和核心专利分析以及重点技术文本聚类分析的基础上,了解国内该技术发展态势,并尝试为脑机接口领域的发展提供有用的竞争情报参考。     

A review on directional information in neural signals for brain-machine interfaces

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.     

A neutral theory with environmental stochasticity explains static and dynamic properties of ecological communities

Understanding the forces shaping ecological communities is crucial to basic science and conservation. Neutral theory has made considerable progress in explaining static properties of communities, like species abundance distributions (SADs), with a simple and generic model, but was criticised for making unrealistic predictions of fundamental dynamic patterns and for being sensitive to interspecific differences in fitness. Here, we show that a generalised neutral theory incorporating environmental stochasticity may resolve these limitations. We apply the theory to real data (the tropical forest of Barro Colorado Island) and demonstrate that it much better explains the properties of short‐term population fluctuations and the decay of compositional similarity with time, while retaining the ability to explain SADs. Furthermore, the predictions are considerably more robust to interspecific fitness differences. Our results suggest that this integration of niches and stochasticity may serve as a minimalistic framework explaining fundamental static and dynamic characteristics of ecological communities.     

Semi-supervised joint spatio-temporal feature selection for P300-based BCI speller

In this paper, we address the important problem of feature selection for a P300-based brain computer interface (BCI) speller system in several aspects. Firstly, time segment selection and electroencephalogram channel selection are jointly performed for better discriminability of P300 and background signals. Secondly, in view of the situation that training data with labels are insufficient, we propose an iterative semi-supervised support vector machine for joint spatio-temporal feature selection as well as classification, in which both labeled training data and unlabeled test data are utilized. More importantly, the semi-supervised learning enables the adaptivity of the system. The performance of our algorithm has been evaluated through the analysis of a P300 dataset provided by BCI Competition 2005 and another dataset collected from an in-house P300 speller system. The results show that our algorithm for joint feature selection and classification achieves satisfactory performance, meanwhile it can significantly reduce the training effort of the system. Furthermore, this algorithm is implemented online and the corresponding results demonstrate that our algorithm can improve the adaptiveness of the P300-based BCI speller.     

Mammalian Insectivores Exert Top‐Down Effects on Azteca Ants

Insectivorous mammals are hypothesized to reduce the abundance of their insect prey. Using a 14‐yr mammal exclusion experiment, we demonstrate for the first time that a widespread and abundant Neotropical mammalian insectivore (Tamandua: Tamandua mexicana) reduced Azteca ant abundance. Azteca ant nests inside mammal exclosures were significantly larger than nests in control plots, where tamanduas were more abundant. These top‐down effects were caused not only by direct consumption, but also through non‐trophic direct effects, specifically nest damage. In contrast, tamanduas appeared to exert no significant top‐down effect on termite prey, which have strong chemical defenses. Our results are consistent with theory that strong defenses against predation can mitigate the top‐down effects of predators on some prey species. We argue that predicting the degree of top‐down effects caused by predators requires both a quantitative knowledge of prey choice and an understanding of the anti‐predator defenses of prey.