Davide Valeriani

Google Inc. | Google

Objective: We aimed at improving group performance in a challenging visual search task via a hybrid collaborative brain-computer interface (cBCI). Methods: Ten participants individually undertook a visual search task where a display was presented for 250 ms, and they had to decide whether a target was present or not. Local temporal correlation c...

This paper presents a hybrid collaborative brain- computer interface (cBCI) to improve group-based recognition of target faces in crowded scenes recorded from surveillance cameras. The cBCI uses a combination of neural features extracted from EEG and response times to estimate the decision confidence of the users. Group decisions are then obtained...

Significance This research identified a microstructural neural network biomarker for objective and accurate diagnosis of isolated dystonia based on the disorder pathophysiology using an advanced deep learning algorithm, DystoniaNet, and raw structural brain images of large cohorts of patients with isolated focal dystonia and healthy controls. Dysto...

Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create "cyborgs" that improve decision making. Human participa...

When a field is new, there is often scarce direct precedent or guidance available for informing ethical decisions. This is compounded by many unknowns encountered during novel technology and application development. As a result, pioneering companies who learn as they go and borrow from historical examples often set precedents. When it comes to the...

The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the...

Objective: Critical decisions are made by effective teams that are characterized by individuals who trust each other and know how to best integrate their opinions. Here, we introduce a multimodal BCI to help collaborative teams of humans and an artificial agent achieve more accurate decisions in assessing danger zones during a pandemic scenario....

Task‐specificity in isolated focal dystonias is a powerful feature that may successfully be targeted with therapeutic brain–computer interfaces. While performing a symptomatic task, the patient actively modulates momentary brain activity (disorder signature) to match activity during an asymptomatic task (target signature), which is expected to tran...

The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions...

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering the breadth of topics in BCI (also called brain-machine interface) research. Some workshops provided detailed examinations of methods, hardware, o...

In our previous work at University of Essex, collaborative brain-computer interfaces (cBCIs) have been used to estimate the decision confidence of individuals from their brain signals and behavioral data, thereby making it possible to improve group decision-making. However, such studies used cBCIs in controlled lab conditions, where users were disc...

Neurotechnologies combine neuroscience and engineering to create tools for studying, repairing, and enhancing brain function. Traditionally, researchers have used neurotechnologies, such as Brain-Computer Interfaces (BCIs), as assistive devices, for example to allow locked-in patients to communicate. In the last few decades, non-invasive brain imag...

Speech production relies on the orchestrated control of multiple brain regions. The specific, directional influences within these networks remain poorly understood. We used regression dynamic causal modelling to infer the whole-brain directed (effective) connectivity from functional magnetic resonance imaging data of 36 healthy individuals during t...

In order to facilitate communication and collaboration between researchers, Brain–computer interfaces (BCI) require a generally applicable functional model as well as a common vocabulary. The IEEE P2731 working group is in the process of developing such a functional model and a lexicon of BCI terminology. Such a functional model has multiple aspect...

The development of Brain–Computer Interfaces (BCIs) requires specialists in various fields, including engineering, computer science, medicine and neuroscience. Each of these disciplines possesses a specific and sometimes differing terminology, which creates obstacles to mutual understanding and research collaboration. The IEEE P2731 working group a...

The description of Brain-Computer Interfaces (BCI) can lead to confusion because of the high heterogeneity of devices, protocols, and applications. Besides, different professional categories are involved: end-users, clinicians, therapists, and engineers; each one having different conceptions of BCI-related terms. This can cause misunderstandings an...

In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to...

We introduce Neurable's research on focus using our recently developed Enten EEG headphones. First we quantify Enten's performance on standard EEG protocols, including eyes-closed alpha rhythms, auditory evoked response and the P300 event-related potential paradigm. We show that Enten's performance is on-par with established industry-standard hardw...

Early career researchers (ECRs) are faced with a range of competing pressures in academia, making self-management key to building a successful career. The Organization for Human Brain Mapping undertook a group effort to gather helpful advice for ECRs in self-management. Bielczyk et al.

Neuromatch Academy (NMA) designed and ran a fully online 3-week Computational Neuroscience Summer School for 1757 students with 191 teaching assistants (TAs) working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity...

Objective: In many real-world decision tasks, the information available to the decision maker is incomplete. To account for this uncertainty, we associate a degree of confidence to every decision, representing the likelihood of that decision being correct. In this tudy, we analyse Electroencephalography (EEG) data from 68 participants undertaking...

Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Term...

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and univers...

In this paper we present and test collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of group decision-making in realistic situations. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide...

We present a two-layered collaborative Brain-Computer Interface (cBCI) to aid groups making decisions under time constraints in a realistic video surveillance setting - the very first cBCI application of this type. The cBCI first uses response times (RTs) to estimate the decision confidence the user would report after each decision. Such an estimat...

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Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technol...

The field of brain–computer interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of ever faster and more reliable assistive technologies for converting brain activity into control signals for external devices for people with severe disabilities [...]

Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create "cyborgs" that improve decision making. Human participa...

The great improvements in brain–computer interface (BCI) performance that are brought upon by merging brain activity from multiple users have made this a popular strategy that allows even for human augmentation. These multi-mind BCIs have contributed in changing the role of BCIs from assistive technologies for people with disabilities into tools fo...

Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic vis...

In the recent years, collaborative Brain-Computer Interfaces (cBCIs) have shown the potential to be used in the context of neuroergonomics to augment human performance, for example in decision making. This study proposes an innovative hybrid cBCI to augment group performance in decision making.

Collaborative brain-computer interfaces (cBCIs) have shown potential to improve group decisions with visual stimuli. This paper proposes a cBCI that assists and improves group decisions in a speech perception task. Neural features extracted from left-temporal-lobe EEG signals and response times were used to estimate the confidence of each individua...

The development of mobile technology over the last years and the consequent boom of available apps has enabled users to migrate a wide range of activities that were traditionally performed on computers to their smartphones. Despite this new freedom to work ubiquitously, there are circumstances in which operating the device becomes difficult, e.g.,...

A Brain-Computer Interface (BCI) provides an alternative means of communication for people who are locked-in. For a BCI to work, the user will perform a specific mental task whilst wearing an Electroencephalography (EEG) cap that contains several electrodes. In particular, in a Motor Imagery (MI) BCI, users imagine themselves performing specific mo...

In this paper we use a collaborative brain- computer interface to integrate the decision confidence of multiple non-communicating observers as a mechanism to improve group decisions. In recent research we tested this idea with the decisions associated with a simple visual matching task and found that a collaborative BCI can outperform group decisio...

Detecting a target in a complex environment can be a difficult task, both for a single individual and a group, es- pecially if the scene is very rich of structure and there are strict time constraints. In recent research, we have demonstrated that collaborative Brain-Computer Interfaces (cBCIs) can use neural signals and response times to estimate...

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a sim...

Robot competitions are effective means to learn the issues of autonomous systems on the field, by solving a complex problem end-to-end. In this paper, we illustrate Red Beard Button, the robotic system that we developed for the Sick Robot Day 2012 competition, and we highlight notions about design and implementation of robotic systems acquired thro...

This paper presents a two-phase method to segment the hippocampus in histological images. The first phase represents a training stage where, from a training set of manually labelled images, the hippocampus representative shape and texture are derived. The second one, the proper segmentation, uses a metaheuristic to evolve the contour of a geometric...

In this paper, we illustrate a viewpoint planning and local navigation algorithm for mobile robot exploration using 3D perception. Laser scans and stereo camera data are read and composed into a single point cloud. The latest acquired point clouds are registered and used to detect relevant objects and obstacles in space. To demonstrate the capabili...