Matthew Bryan's research while affiliated with University of Washington Seattle and other places

Publications (6)

Preprint
Full-text available
Objective: A major challenge in closed-loop brain-computer interfaces (BCIs) is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Traditional approaches, such as those currently used for deep brain stimulation, have largely followed a trial- and-error strategy to search for effectiv...
Article
Full-text available
We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the br...
Article
Objective: Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach: We introduce a...
Article
Recent advances in neuroscience and robotics have allowed initial demonstrations of brain-computer interfaces (BCIs) for controlling wheeled and humanoid robots. However, further advances have proved challenging due to the low throughput of the interfaces and the high degrees-of-freedom (DOF) of the robots. In this paper, we build on our previous w...
Conference Paper
Recent advances in neuroscience and humanoid robotics have allowed initial demonstrations of brain-computer interfaces (BCIs) for controlling humanoid robots. However, previous BCIs have relied on higher-level control based on fixed pre-wired behaviors. On the other hand, low-level control can be tedious, imposing a high cognitive load on the BCI u...

Citations

... Still, in the context of BCI, other social environments were created with the assistance of ML algorithms. For example, Rao et al. (2014) proposed the brain-to-brain interface in humans, where the EEG signals from one user were used to stimulate the brain of a second subject through transcranial magnetic stimulation (TMS). Later, this concept was expanded for the idea of a "brainnet," where the signals of some users (senders) were collaboratively merged to stimulate the brain of an independent participant (receiver) . ...
... To the very best knowledge of the authors, only one paper has implemented a POMDP model based BCI system [34], though several studies have been conducted on the the more broad field of human-machine interaction (HMI). Specifically, some work has been conducted on implementing mixed-observability MDP (MOMDP) systems to model the collaboration between a human agent and autonomous machines, while considering the cognitive state of the human in search and rescue [35] or target search [36] scenarios. ...
... Likewise, methodologies presented in References [5] and [43] require computationally intensive vision processing that involves extensive information processing. The work in Reference [17] addresses controlling wheeled and humanoid robots using a BMI. The authors present algorithms for learning command hierarchies using the history of the commands generated by the user. ...
... Waytowich et al. (2010) used EEG signals to control pick and place operations of a four-DoF Stäubli robot. Bryan et al. (2011) presented preliminary work, extending this approach to a grasping pipeline on the PR2 robot. In that work, a 3D perception pipeline was used to find and identify target objects for grasping and EEG signals were used to choose between them. ...
... In addition, BCI systems driven by external stimuli are routinely used in distance learning, such as Steady State Visually Induced Potential (SSVEP). High data transfer (ITR) and excellent performance with few or no training sessions [25][26][27][28]. Abibullaev et al. [29] designed an event-related potential (ERP) measured by EEG in healthy volunteers to control an endogenous humanoid telepresence robot with distance presence. ...