Decoding Multisensory Attention from Electroencephalography for Use in a Brain-Computer Interface

Channel:
Microsoft Research
Subscribers:
344,000
Published on ● Video Link: https://www.youtube.com/watch?v=GOpIyQKGDD4


Category:
Vlog
Duration: 1:10:51
2,587 views
79

Brain-computer interfaces (BCIs) offer a non-verbal and covert way for humans to interact with a machine. They are designed to interpret user’s brain state that can be translated into action or other communication purposes. While most previous BCI studies focus on motor imagery or steady-state evoked potential paradigms, we investigated the feasibility of a BCI system using a task-based paradigm that can potentially be integrated in a more user-friendly and engaging manner. The user was presented with multiple simultaneous, spatially separated streams of auditory and/or tactile stimuli and directed to detect a pattern in one particular stream. We applied a model-free method to decode the stream tracking effort from the EEG signal. The results showed that the proposed BCI system could capture attention from most of the participants using multisensory inputs. Successful decoding in real-time would allow the user to communicate a “control” signal to the computer via attention.

Talk slides: https://www.microsoft.com/en-us/research/uploads/prod/2019/09/Decoding-Multisensory-Attention-from-Electroencephalography-for-Use-in-a-Brain-Computer-Interface-SLIDES.pdf

Learn more about this and other talks at Microsoft Research: https://www.microsoft.com/en-us/research/video/decoding-multisensory-attention-from-electroencephalography-for-use-in-a-brain-computer-interface/



Other Videos By Microsoft Research


2019-10-04Scheduling For Efficient Large-Scale Machine Learning Training
2019-10-03Distributed Entity Resolution for Computational Social Science
2019-10-03MMLSpark: empowering AI for Good with Mark Hamilton [Podcast]
2019-10-02Non-linear Invariants for Control-Command Systems
2019-10-02Vision-and-Dialog Navigation
2019-10-01The Future of Mathematics?
2019-09-30How Not to Prove Your Election Outcome
2019-09-30The Worst Form Including All Those Others: Canada’s Experiments with Online Voting
2019-09-30DIFF: A Relational Interface for Large-Scale Data Explanation
2019-09-30A Calculus for Brain Computation
2019-09-26Decoding Multisensory Attention from Electroencephalography for Use in a Brain-Computer Interface
2019-09-26A Short Introduction to DIMACS & DIMACS and MSR-NYC
2019-09-26Boosting Innovation and Discovery of Ideas
2019-09-26Resource-Efficient Redundancy for Large-Scale Data Processing and Storage Systems
2019-09-26Optimizing Declarative Graph Queries at Large Scale
2019-09-25SILK: Preventing Latency Spikes in Log-Structured Merge Key-Value Stores
2019-09-25Coverage Guided, Property Based Testing
2019-09-25Efficient Robot Skill Learning: Grounded Simulation Learning and Imitation Learning from Observation
2019-09-25Towards Secure and Interpretable AI: Scalable Methods, Interactive Visualizations, & Practical Tools
2019-09-25Sequential Estimation of Quantiles with Applications to A/B-testing and Best-arm Identification
2019-09-25Reproducible Codes and Cryptographic Applications


Tags:
Brain-computer interfaces
BCI
EEG signal
multisensory
HCI
Electroencephalography
human-computer interface
Wenkang An
Microsoft Research
MSR