OBJECTIVE: Electrocorticography (ECoG) has been used for a range of applications including electrophysiological mapping, epilepsy monitoring, and more recently as a recording modality for brain-computer interfaces (BCIs). Studies that examine ECoG electrodes designed and implanted chronically solely for BCI applications remain limited. The present study explored how two key factors influence chronic, closed-loop ECoG BCI: (i) the effect of inter-electrode distance on BCI performance and (ii) the differences in neural adaptation and performance when fixed versus adaptive BCI decoding weights are used. APPROACH: The amplitudes of epidural micro-ECoG signals between 75 and 105 Hz with 300 μm diameter electrodes were used for one-dimensional and two-dimensional BCI tasks. The effect of inter-electrode distance on BCI control was tested between 3 and 15 mm. Additionally, the performance and cortical modulation differences between constant, fixed decoding using a small subset of channels versus adaptive decoding weights using the entire array were explored. MAIN RESULTS: Successful BCI control was possible with two electrodes separated by 9 and 15 mm. Performance decreased and the signals became more correlated when the electrodes were only 3 mm apart. BCI performance in a 2D BCI task improved significantly when using adaptive decoding weights (80%-90%) compared to using constant, fixed weights (50%-60%). Additionally, modulation increased for channels previously unavailable for BCI control under the fixed decoding scheme upon switching to the adaptive, all-channel scheme. SIGNIFICANCE: Our results clearly show that neural activity under a BCI recording electrode (which we define as a 'cortical control column') readily adapts to generate an appropriate control signal. These results show that the practical minimal spatial resolution of these control columns with micro-ECoG BCI is likely on the order of 3 mm. Additionally, they show that the combination and interaction between neural adaptation and machine learning are critical to optimizing ECoG BCI performance.
OBJECTIVE: Electrocorticography (ECoG) has been used for a range of applications including electrophysiological mapping, epilepsy monitoring, and more recently as a recording modality for brain-computer interfaces (BCIs). Studies that examine ECoG electrodes designed and implanted chronically solely for BCI applications remain limited. The present study explored how two key factors influence chronic, closed-loop ECoG BCI: (i) the effect of inter-electrode distance on BCI performance and (ii) the differences in neural adaptation and performance when fixed versus adaptive BCI decoding weights are used. APPROACH: The amplitudes of epidural micro-ECoG signals between 75 and 105 Hz with 300 μm diameter electrodes were used for one-dimensional and two-dimensional BCI tasks. The effect of inter-electrode distance on BCI control was tested between 3 and 15 mm. Additionally, the performance and cortical modulation differences between constant, fixed decoding using a small subset of channels versus adaptive decoding weights using the entire array were explored. MAIN RESULTS: Successful BCI control was possible with two electrodes separated by 9 and 15 mm. Performance decreased and the signals became more correlated when the electrodes were only 3 mm apart. BCI performance in a 2D BCI task improved significantly when using adaptive decoding weights (80%-90%) compared to using constant, fixed weights (50%-60%). Additionally, modulation increased for channels previously unavailable for BCI control under the fixed decoding scheme upon switching to the adaptive, all-channel scheme. SIGNIFICANCE: Our results clearly show that neural activity under a BCI recording electrode (which we define as a 'cortical control column') readily adapts to generate an appropriate control signal. These results show that the practical minimal spatial resolution of these control columns with micro-ECoG BCI is likely on the order of 3 mm. Additionally, they show that the combination and interaction between neural adaptation and machine learning are critical to optimizing ECoG BCI performance.
Authors: Daniel B Silversmith; Reza Abiri; Nicholas F Hardy; Nikhilesh Natraj; Adelyn Tu-Chan; Edward F Chang; Karunesh Ganguly Journal: Nat Biotechnol Date: 2020-09-07 Impact factor: 54.908
Authors: Nicholas Rogers; John Hermiz; Mehran Ganji; Erik Kaestner; Kıvılcım Kılıç; Lorraine Hossain; Martin Thunemann; Daniel R Cleary; Bob S Carter; David Barba; Anna Devor; Eric Halgren; Shadi A Dayeh; Vikash Gilja Journal: PLoS Comput Biol Date: 2019-02-11 Impact factor: 4.475
Authors: Virginia Woods; Michael Trumpis; Brinnae Bent; Kay Palopoli-Trojani; Chia-Han Chiang; Charles Wang; Chunxiu Yu; Michele N Insanally; Robert C Froemke; Jonathan Viventi Journal: J Neural Eng Date: 2018-09-24 Impact factor: 5.379
Authors: Chia-Han Chiang; Charles Wang; Katrina Barth; Shervin Rahimpour; Michael Trumpis; Suseendrakumar Duraivel; Iakov Rachinskiy; Agrita Dubey; Katie E Wingel; Megan Wong; Nicholas S Witham; Thomas Odell; Virginia Woods; Brinnae Bent; Werner Doyle; Daniel Friedman; Eckardt Bihler; Christopher F Reiche; Derek G Southwell; Michael M Haglund; Allan H Friedman; Shivanand P Lad; Sasha Devore; Orrin Devinsky; Florian Solzbacher; Bijan Pesaran; Gregory Cogan; Jonathan Viventi Journal: J Neural Eng Date: 2021-06-17 Impact factor: 5.043