3rd International IEEE EMBS Conference on Neural Engineering

2006 ◽  
Vol 53 (7) ◽  
pp. 1454-1454
Keyword(s):  
Neural Engineering

Guest Editorial: Special Issue on Haptics in Rehabilitation and Neural Engineering

2014 ◽  
Vol 7 (2) ◽  
pp. 107-109
Author(s):  
Antonio Frisoli ◽  
Marcia OrMalley ◽  
Domenico Campolo ◽  
Kathleen Sienko
Keyword(s):  
Guest Editorial ◽  
Neural Engineering ◽  
Special Issue ◽  
Editorial Special Issue

scholarly journals Impacts of a Neural Engineering Summer Research Experience on High School Students (Evaluation)

2015 ◽  
Author(s):  
Kristen Clapper Bergsman ◽  
Eric Chudler ◽  
Laura Collins ◽  
Jill Weber ◽  
Lise Johnson
Keyword(s):  
High School ◽  
High School Students ◽  
Neural Engineering ◽  
Research Experience ◽  
School Students

scholarly journals Exploring Neural Engineering with a Teacher-Authored Science Curriculum (Curriculum Exchange)

2015 ◽  
Author(s):  
Kristen Clapper Bergsman ◽  
Eric Chudler ◽  
Shannon Jephson-Hernandez ◽  
Michael Shaw ◽  
Lise Johnson
Keyword(s):  
Science Curriculum ◽  
Neural Engineering

scholarly journals Evolutionary Multitasking-Based Multiobjective Optimization Algorithm for Channel Selection in Hybrid Brain Computer Interfacing Systems

2021 ◽  
Vol 15 ◽  
Author(s):  
Tianyu Liu ◽  
Zhixiong Xu ◽  
Lei Cao ◽  
Guowei Tan
Keyword(s):  
Multiobjective Optimization ◽  
Real Life ◽  
Channel Selection ◽  
Research Field ◽  
Neural Engineering ◽  
Computer Interfaces ◽  
Classification Tasks ◽  
Optimization Methodology ◽  
Brain Computer Interfacing ◽  
Selection Of

Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in the research field of neural engineering. The number of channels should be as small as possible for real-life applications. However, most of recent works about channel selection only focus on either the performance of classification task or the effectiveness of device control. Few works conduct channel selection for MI and SSVEP classification tasks simultaneously. In this paper, a multitasking-based multiobjective evolutionary algorithm (EMMOA) was proposed to select appropriate channels for these two classification tasks at the same time. Moreover, a two-stage framework was introduced to balance the number of selected channels and the classification accuracy in the proposed algorithm. The experimental results verified the feasibility of multiobjective optimization methodology for channel selection of hybrid BCI tasks.

Graduate program in neural engineering

2002 ◽  
Author(s):  
S.S. Devgan ◽  
M. Malkani ◽  
M. Bodruzzaman ◽  
H.K. Rucker
Keyword(s):  
Graduate Program ◽  
Neural Engineering
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