The Riemannian spatial pattern method: mapping and clustering movement imagery using Riemannian geometry

Larzabal, Christelle; Auboiroux, Vincent; Karakas, Serpil; Charvet, Guillaume; Benabid, Alim‐Louis; Chabardès, Stéphan; Costecalde, Thomas; Bonnet, Stéphane

doi:10.1088/1741-2552/abf291

Cited by 12 publications

(14 citation statements)

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“…Achieving higher classification accuracies is likely possible as different features, feature/electrode selection methods and machine learning models can be employed. Indeed, methods such as Riemannian spatial patterns ( Larzabal et al, 2021 ) may also be applied to gain insights in which electrodes are most important for decoding individual finger movements.…”

Section: Discussionmentioning

confidence: 99%

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system

Lee

Schreiner

et al. 2022

Front. Neurosci.

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Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10–20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8–12 Hz) and beta (13–25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.

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Section: Discussionmentioning

confidence: 99%

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system

Lee

Schreiner

et al. 2022

Front. Neurosci.

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“…Several human and NHP epidural studies with clinical and high‐density grids have accomplished levels of decoding of upper limb movements (Baker et al, 2009; Choi, Kim, et al, 2018; Choi, Lee, Park, Lee, et al, 2018; Larzabal et al, 2021; Spüler, Walter, Murguialday, et al, 2014; Thomas et al, 2019) that are comparable with those for subdural electrodes (Gruenwald et al, 2019; Jiang et al, 2017, 2018; Kubánek et al, 2009; Shiraishi et al, 2020; Yao & Shoaran, 2019). Of note, one study described both epidural and subdural classifications of finger, wrist and elbow flexion and extension in abled‐bodied participants using clinical and high‐density grids (Thomas et al, 2019).…”

Section: Signal Decodability For Epidural Recordingsmentioning

confidence: 99%

“…Several human and NHP epidural studies with clinical and high-density grids have accomplished levels of decoding of upper limb movements (Baker et al, 2009;Larzabal et al, 2021;Spüler, Walter, Murguialday, et al, 2014;Thomas et al, 2019) that are comparable with those for subdural electrodes (Gruenwald et al, 2019;Jiang et al, 2017Jiang et al, , 2018Kub anek et al, 2009;Shiraishi et al, 2020;Yao & Shoaran, 2019). Of note, one study…”

Section: Offline Classification Studiesmentioning

confidence: 99%

“…This study showed an epidural accuracy (93%) in the same range as the subdural accuracies (71–100%). Other examples of epidural studies are Larzabal et al (2021), who reported up to 60% classification accuracy for 12 imagined upper‐limb movements, including finger movements, from long‐term high‐density epidural ECoG in an individual with tetraplegia, and Spüler, Walter, Murguialday, et al (2014), who decoded seven classes (on average 61% accuracy) in severely paralysed individuals implanted with epidural clinical grids, at a level similar to that described for subdural counterparts (e.g., 80% accuracy for five classes; Kubánek et al, 2009). In Spüler, Walter, Murguialday, et al (2014), the high‐frequency band contributed most to the decoding accuracy, and, importantly, frequencies up to 150 Hz still contributed useful information.…”

Section: Signal Decodability For Epidural Recordingsmentioning

confidence: 99%

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Nine decades of electrocorticography: A comparison between epidural and subdural recordings

Branco

Geukes

Aarnoutse

et al. 2023

Eur J of Neuroscience

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In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long‐term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non‐human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high‐density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long‐term neural signal recordings, at least for current generations of clinical and high‐density ECoG grids.

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“…The clinical trial with bilateral implants has enrolled two patients so far [108]. Training was progressive by adding more complexity in the adaptive machine learning algorithm, from brain switch to 3D + pronation/supination [322]. The signal proved to be stable over months.…”

Section: Lessons From Successfully Implanted Neurotechnologymentioning

confidence: 99%

Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier

Huggins

Krusienski

Vansteensel

et al. 2022

Brain-Computer Interfaces

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The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering the breadth of topics in BCI (also called brain-machine interface) research. Some workshops provided detailed examinations of methods, hardware, or processes. Others focused on BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and improve comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, summarizes discussions, and describes the resulting conclusion, challenges, or initiatives.

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The Riemannian spatial pattern method: mapping and clustering movement imagery using Riemannian geometry

Cited by 12 publications

References 65 publications

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system

Nine decades of electrocorticography: A comparison between epidural and subdural recordings

Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier

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