An optical brain computer interface for environmental control

A Motivational Model of BCI-Controlled Heuristic Search

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain⁻Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user's disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.

Utah optrode array customization using stereotactic brain atlases and 3-D CAD modeling for optogenetic neocortical interrogation in small rodents and nonhuman primates

As the optogenetic field expands, the need for precise targeting of neocortical circuits only grows more crucial. This work demonstrates a technique for using Solidworks^® computer-aided design (CAD) and readily available stereotactic brain atlases to create a three-dimensional (3-D) model of the dorsal region of area visual cortex 4 (V4D) of the macaque monkey (Macaca fascicularis) visual cortex. The 3-D CAD model of the brain was used to customize an [Formula: see text] Utah optrode array (UOA) after it was determined that a high-density ([Formula: see text]) UOA caused extensive damage to marmoset (Callithrix jacchus) primary visual cortex as assessed by electrophysiological recording of spiking activity through a 1.5-mm-diameter through glass via. The [Formula: see text] UOA was customized for optrode length ([Formula: see text]), optrode width ([Formula: see text]), optrode pitch ([Formula: see text]), backplane thickness ([Formula: see text]), and overall form factor ([Formula: see text]). Two [Formula: see text] UOAs were inserted into layer VI of macaque V4D cortices with minimal damage as assessed in fixed tissue cytochrome oxidase staining in nonrecoverable surgeries. Additionally, two [Formula: see text] arrays were implanted in mice (Mus musculus) motor cortices, providing early evidence for long-term tolerability (over 6 months), and for the ability to integrate the UOA with a Holobundle light delivery system toward patterned optogenetic stimulation of cortical networks.

Maskless wafer-level microfabrication of optical penetrating neural arrays out of soda-lime glass: Utah Optrode Array

Borrowing from the wafer-level fabrication techniques of the Utah Electrode Array, an optical array capable of delivering light for neural optogenetic studies is presented in this paper: the Utah Optrode Array. Utah Optrode Arrays are micromachined out of sheet soda-lime-silica glass using standard backend processes of the semiconductor and microelectronics packaging industries such as precision diamond grinding and wet etching. 9 × 9 arrays with 1100μ m × 100μ m optrodes and a 500μ m back-plane are repeatably reproduced on 2i n wafers 169 arrays at a time. This paper describes the steps and some of the common errors of optrode fabrication.

Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface

Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent's facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.

Evaluating a four-class motor-imagery-based optical brain-computer interface

This work investigates the potential of a four-class motor-imagery-based brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS). Four motor imagery tasks (right hand, left hand, right foot, and left foot tapping) were executed while motor cortex activity was recorded via fNIRS. Preliminary results from three participants suggest that this could be a viable BCI interface, with two subjects achieving 50% accuracy. fNIRS is a noninvasive, safe, portable, and affordable optical brain imaging technique used to monitor cortical hemodynamic changes. Because of its portability and ease of use, fNIRS is amenable to deployment in more natural settings. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) BCIs have already been used with up to four motor-imagery-based commands. While fNIRS-based BCIs are relatively new, success with EEG and fMRI systems, as well as signal characteristics similar to fMRI and complementary to EEG, suggest that fNIRS could serve to build or augment future BCIs.

Acute Supramaximal Exercise Increases the Brain Oxygenation in Relation to Cognitive Workload

Single bout of exercise can improve the performance on cognitive tasks. However, cognitive responses may be controversial due to different type, intensity, and duration of exercise. In addition, the mechanism of the effect of acute exercise on brain is still unclear. This study was aimed to investigate the effects of supramaximal exercise on cognitive tasks by means of brain oxygenation monitoring. The brain oxygenation of Prefrontal cortex (PFC) was measured on 35 healthy male volunteers via functional near infrared spectroscopy (fNIRS) system. Subjects performed 2-Back test before and after the supramaximal exercise wingate anerobic test (WAnT) lasting 30-s on cycle ergometer. The PFC oxygenation change evaluation revealed that PFC oxygenation rise during post-exercise 2-Back task was considerably higher than those in pre-exercise 2-Back task. In order to describe the relationship between oxygenation change and exercise performance, subjects were divided into two groups as high performers (HP) and low performers (LP) according to their peak power values (PP) obtained from the supramaximal test. The oxy-hemoglobin (oxy-Hb) values were compared between pre- and post-exercise conditions within subjects and also between subjects according to peak power. When performers were compared, in the HP group, the oxy-Hb values in post-exercise 2-Back test were significantly higher than those in pre-exercise 2-Back test. HP had significantly higher post-exercise oxy-Hb change (Δ) than those of LP. In addition, PP of the total group were significantly correlated with Δoxy-Hb.The key findings of the present study revealed that acute supramaximal exercise has an impact on the brain oxygenation during a cognitive task. Also, the higher the anerobic PP describes the larger the oxy-Hb response in post-exercise cognitive task. The current study also demonstrated a significant correlation between peak power (exercise load) and post-exercise hemodynamic responses (oxy-, deoxy- and total-Hb). The magnitude of this impact might be related with the physical performance capacities of the individuals. This can become a valuable parameter for future studies on human factor.

Non-invasive control interfaces for intention detection in active movement-assistive devices

Active movement-assistive devices aim to increase the quality of life for patients with neuromusculoskeletal disorders. This technology requires interaction between the user and the device through a control interface that detects the user’s movement intention. Researchers have explored a wide variety of invasive and non-invasive control interfaces. To summarize the wide spectrum of strategies, this paper presents a comprehensive review focused on non-invasive control interfaces used to operate active movement-assistive devices. A novel systematic classification method is proposed to categorize the control interfaces based on: (I) the source of the physiological signal, (II) the physiological phenomena responsible for generating the signal, and (III) the sensors used to measure the physiological signal. The proposed classification method can successfully categorize all the existing control interfaces providing a comprehensive overview of the state of the art. Each sensing modality is briefly described in the body of the paper following the same structure used in the classification method. Furthermore, we discuss several design considerations, challenges, and future directions of non-invasive control interfaces for active movement-assistive devices.

A P300-based EEG-BCI for spatial navigation control

In this study, a Brain Computer Interface (BCI) based on the P300 oddball paradigm has been developed for spatial navigation control in virtual environments. Functionality and efficacy of the system were analyzed with results from nine healthy volunteers. Each participant was asked to gaze at an individual target in a 3×3 P300 matrix containing different symbolic navigational icons while EEG signals were collected. Resulting ERPs were processed online and classification commands were executed to control spatial movements within the MazeSuite virtual environment and presented to the user online during an experiment. Subjects demonstrated on average, ∼89% online accuracy for simple mazes and ∼82% online accuracy in longer more complex mazes. Results suggest that this BCI setup enables guided free-form navigation in virtual 3D environments.

Error detection and error memory in spatial navigation as reflected by electrodermal activity

The study investigated spatial navigation by means of electrodermal activity (EDA). Two groups of healthy subjects (group 1, age <38; group 2, age ≥ 38) were recorded during navigation through two 3-D virtual mazes differing in difficulty, that is, Maze Simple (MazeS) and Maze Complex (MazeC). Our results show (1) an effect of difficulty, that is, larger skin conductance responses (SCRs) and slower velocity profiles while navigating through MazeC as compared to MazeS. (2) An effect of age, that is, larger SCRs and faster velocity profiles in younger subjects (group 1) compared to older subjects (group 2). (3) An effect of maze region, that is, SCRs increased when subjects entered dead ends with group 1 (young group) decreasing in velocity, whereas group 2 (old group) increased in velocity. (4) And an error memory effect, that is, subjects who remembered an error at a given decision point (crossroads preceding dead ends in MazeC) from previous trials, and then if they did not repeat that error, elicited decreased SCRs as compared to subjects who did not remember and subsequently repeated an error. The latter aspect is the most impactful as it shows that EDA is able to reflect error detection and memory during spatial navigation. Our data designate EDA as suitable monitoring tool for identification and differentiation of the affective correlates underlying spatial navigation, which has recently attracted researchers' attention due to its increased use in 3-D virtual environments.