OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) is a promising platform for controlling arm prostheses. To restore functional independence, a BCI must be able to control arm prostheses along at least six degrees-of-freedoms (DOFs). Prior studies suggest that standard ECoG grids may be insufficient to decode multi-DOF arm movements. This study compared the ability of standard and high-density (HD) ECoG grids to decode the presence/absence of six elementary arm movements and the type of movement performed. APPROACH: Three subjects implanted with standard grids (4 mm diameter, 10 mm spacing) and three with HD grids (2 mm diameter, 4 mm spacing) had ECoG signals recorded while performing the following movements: (1) pincer grasp/release, (2) wrist flexion/extension, (3) pronation/supination, (4) elbow flexion/extension, (5) shoulder internal/external rotation, and (6) shoulder forward flexion/extension. Data from the primary motor cortex were used to train a state decoder to detect the presence/absence of movement, and a six-class decoder to distinguish between these movements. MAIN RESULTS: The average performances of the state decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those of their standard grid counterparts across all combinations of the μ, β, low-γ, and high-γ frequency bands. The average best decoding error for HD grids was 2.6%, compared to 8.5% of standard grids (chance 50%). The movement decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those based on standard ECoG across all band combinations. The average best decoding errors of 11.9% and 33.1% were obtained for HD and standard grids, respectively (chance error 83.3%). These improvements can be attributed to higher electrode density and signal quality of HD grids. SIGNIFICANCE: Commonly used ECoG grids are inadequate for multi-DOF BCI arm prostheses. The performance gains by HD grids may eventually lead to independence-restoring BCI arm prosthesis.
OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) is a promising platform for controlling arm prostheses. To restore functional independence, a BCI must be able to control arm prostheses along at least six degrees-of-freedoms (DOFs). Prior studies suggest that standard ECoG grids may be insufficient to decode multi-DOF arm movements. This study compared the ability of standard and high-density (HD) ECoG grids to decode the presence/absence of six elementary arm movements and the type of movement performed. APPROACH: Three subjects implanted with standard grids (4 mm diameter, 10 mm spacing) and three with HD grids (2 mm diameter, 4 mm spacing) had ECoG signals recorded while performing the following movements: (1) pincer grasp/release, (2) wrist flexion/extension, (3) pronation/supination, (4) elbow flexion/extension, (5) shoulder internal/external rotation, and (6) shoulder forward flexion/extension. Data from the primary motor cortex were used to train a state decoder to detect the presence/absence of movement, and a six-class decoder to distinguish between these movements. MAIN RESULTS: The average performances of the state decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those of their standard grid counterparts across all combinations of the μ, β, low-γ, and high-γ frequency bands. The average best decoding error for HD grids was 2.6%, compared to 8.5% of standard grids (chance 50%). The movement decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those based on standard ECoG across all band combinations. The average best decoding errors of 11.9% and 33.1% were obtained for HD and standard grids, respectively (chance error 83.3%). These improvements can be attributed to higher electrode density and signal quality of HD grids. SIGNIFICANCE: Commonly used ECoG grids are inadequate for multi-DOF BCI arm prostheses. The performance gains by HD grids may eventually lead to independence-restoring BCI arm prosthesis.
Authors: Tessy M Thomas; Daniel N Candrea; Matthew S Fifer; David P McMullen; William S Anderson; Nitish V Thakor; Nathan E Crone Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2019-01-07 Impact factor: 3.802
Authors: Mariana P Branco; Michael Leibbrand; Mariska J Vansteensel; Zachary V Freudenburg; Nick F Ramsey Journal: Neuroimage Date: 2018-06-18 Impact factor: 6.556
Authors: Po T Wang; Colin M McCrimmon; Christine E King; Susan J Shaw; David E Millett; Hui Gong; Luis A Chui; Charles Y Liu; Zoran Nenadic; An H Do Journal: Brain Struct Funct Date: 2017-05-18 Impact factor: 3.270
Authors: Mariana P Branco; Anna Gaglianese; Daniel R Glen; Dora Hermes; Ziad S Saad; Natalia Petridou; Nick F Ramsey Journal: J Neurosci Methods Date: 2017-11-01 Impact factor: 2.390
Authors: Colin M McCrimmon; Po T Wang; Payam Heydari; Angelica Nguyen; Susan J Shaw; Hui Gong; Luis A Chui; Charles Y Liu; Zoran Nenadic; An H Do Journal: Cereb Cortex Date: 2018-08-01 Impact factor: 5.357
Authors: Mariana P Branco; Zachary V Freudenburg; Erik J Aarnoutse; Mariska J Vansteensel; Nick F Ramsey Journal: Biomed Phys Eng Express Date: 2018-05-17