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Bi-Band ECoGNet for ECoG Decoding on Classification Task

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Bi-Band ECoGNet replaces the costly MST transform with learned convolutions to decode visual ECoG faster and slightly more accurately.

desk verdict Incremental ECoG architecture with a credible speedup but an unsupported accuracy gain due to in-sample hyperparameter selection. read the letter →

arxiv 2412.00378 v3 pith:SGE5XALV submitted 2024-11-30 math.NA cs.CVcs.NA

Changqing Ji This is my paper · ORCID
classification math.NAcs.CVcs.NA
keywords ECoGdecodingbrain-computerinterfacevisualclassificationmulti-classtemporal-frequencyfeatureextractionBi-BandGNet2Dspatialencoderconvolutionalneuralnetwork
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes Bi-Band ECoGNet, a compact neural network for decoding visual categories from electrocorticography (ECoG) signals recorded with an 8-by-16 electrode array. It replaces the slow Modified Stockwell Transform (MST) used in prior work with a learnable Bi-Band Channel-Wise Transform made of 64 temporal convolutions, half with kernel length 512 and half with length 32, so the network extracts both low- and high-frequency features directly from raw signals. It also reshapes each frequency feature map back into the electrode-grid layout and applies 2D convolutions to capture spatial structure that a 1D spatial filter would miss. On the two macaque visual ECoG datasets, the model reaches 54.15% and 35.98% accuracy respectively, about 1.24% higher on average than MST-ECoGNet while keeping the same parameter count and training about six times faster. The reason a sympathetic reader would care is that the result suggests the hand-designed time-frequency transform can be replaced by a small learned module without losing decoding power, which makes invasive BCI decoding more practical.

What carries the argument

The central machinery is the Bi-Band Channel-Wise Transform (Bi-BCWT), a bank of 64 channel-wise one-dimensional temporal convolutions with two kernel lengths, 512 and 32, whose learned parameters substitute for the window function of the Modified Stockwell Transform. Half the kernels cover low frequencies, half cover high frequencies, and their outputs are concatenated into a 3D frequency-spatial-temporal feature space. The companion mechanism is the Spatial-Temporal Feature Encoder, which reshapes each feature map into the physical 8-by-16 electrode layout and applies two 2D convolutions, so the network uses the electrode array's geometry as an inductive bias. Together they replace the two most expensive or rigid parts of the prior MST-ECoGNet: the hand-tuned MST time-frequency analysis and the 1D spatial filter.

What would settle it

Train Bi-Band ECoGNet and MST-ECoGNet on the same two subjects but choose all hyperparameters (kernel lengths, TCN count) on a separate validation split or via nested cross-validation, then compare accuracies on a held-out test set; if the difference is not at least 1.24% on average, or if a model with only one kernel length matches the two-band version on out-of-sample data, the central claim of a frequency-complementarity advantage would be refuted.

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Extended reading notes

Core claim

The central claim is that a fully learned, two-band temporal-convolution front end can replace the expensive MST time-frequency analysis in ECoG decoding while preserving or improving accuracy. The Bi-Band ECoGNet's Bi-BCWT module uses 64 channel-wise 1D convolutions, 32 with kernel length 512 that attend to low-frequency content and 32 with kernel length 32 that attend to higher-frequency content; an FIR band-pass probing experiment confirms these frequency preferences. Each resulting feature map is reshaped from 128 channels into an 8-by-16 grid matching the electrode array, then passed through two 2D convolutional layers (8-by-8-by-1 and 1-by-2-by-1 kernels) that collapse the spatial dimensions into a 1-by-1-by-T vector, extracting the 2D spatial pattern that the 1D spatial filter of MST-ECoGNet ignores. The paper reports that on subject MonC accuracy rises from 53.43% to 54.15% and on MonJ from 34.22% to 35.98%, at equal or smaller model size (0.0396M parameters), with training time per epoch dropping from 189s to 33s (MonC) and 139s to 21.2s (MonJ). It also reports ablation results showing mixed kernel lengths outperform single lengths, and the 2D encoder outperforms the 1D alternative by 1.04%.

Load-bearing premise

The load-bearing premise is that choosing the two kernel lengths (32 and 512) and the 64 TCNs based on the same dataset's test accuracy did not bias the cross-validated results upward; if that selection used the same folds as the final evaluation, the reported 1.24% advantage over MST-ECoGNet could shrink or disappear outside this dataset.

Editorial extensions

If this is right

  • Hand-designed time-frequency transforms can be replaced by learned convolutions in ECoG decoding, removing the need to precompute and store large 3D feature tensors.
  • A model of 0.0396M parameters with training at roughly 33 seconds per epoch becomes feasible for real-time or repeated BCI retraining on laboratory hardware.
  • The 2D spatial encoder's gain shows that preserving the physical electrode grid layout during feature extraction improves classification, a design choice that transfers to other grid-based neural recordings.
  • The frequency-importance ablation indicates that discriminative visual ECoG information concentrates in low-frequency bands, with high-frequency bands near random-guess accuracy, which can guide where to allocate model capacity.
  • Subject-cross results suggest models trained on one animal do not transfer to another, implicating subject-specific spatial patterns; this motivates channel selection or adaptation as the next step.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The in-sample selection of kernel lengths (32 and 512) from the same evaluation dataset may inflate the reported gains; a nested cross-validation or a fully held-out subject would give a more conservative estimate of the 1.24% improvement.
  • The FIR band-pass probing method could be turned into a diagnostic tool for other ECoG decoding models to map which frequency bands drive each model's predictions, allowing principled kernel design beyond two bands.
  • If the low-frequency concentration is general, then downsampling or low-pass filtering ECoG before training might reduce computation further without hurting accuracy, and simpler non-deep baselines might close part of the gap.
  • The channel heat maps suggest automated channel selection could shrink the 128-channel input to a smaller subset, potentially enabling lighter hardware for clinical BCI.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes Bi-Band ECoGNet for visual ECoG classification, replacing the computationally expensive Modified Stockwell Transform (MST) preprocessing with an in-network Bi-Band Channel-Wise Transform (Bi-BCWT) composed of 64 TCN modules with two kernel lengths (512 and 32). The architecture adds a 2D spatial-temporal encoder that exploits the 8x16 electrode layout, followed by an EEGNet-style fusion/classification head. On two monkey subjects (MonC and MonJ), the authors report 54.15% and 35.98% accuracy, an average 1.24% improvement over the MST-ECoGNet baseline, a 6x training speedup, and a smaller model. Ablation studies examine the number of TCNs, kernel length, spatial encoder type, and individual channel contributions.

Significance. If the accuracy gain is real, Bi-Band ECoGNet is a useful lightweight alternative to MST-based ECoG decoding: it replaces a storage-heavy, slow preprocessing step with learned convolutions, and the 2D spatial encoder is a sensible way to exploit the electrode array geometry. The channel-importance and frequency-analysis experiments are interesting exploratory contributions. However, the central quantitative claim is not currently supported. Hyperparameters were selected on the evaluation set, the reported error bars overlap, no significance tests are given, and the ablation tables contain internal inconsistencies. The core idea is promising, but the validation must be redone before the performance claims can be accepted.

major comments (4)
  1. [Section IV-A and Table II] The final hyperparameters (64 TCN modules and kernel lengths 32 and 512) were selected by comparing test accuracy on the same dataset used for the reported final results (Figures 5 and 6, including the statement 'Finally 32 & 512 kernel size are selected, base on the performance'). Consequently, the 5-fold cross-validated accuracies in Table II have already been optimized over these hyperparameters and are not independent out-of-sample estimates; the reported 1.24% average improvement over MST-ECoGNet may be inflated by selection bias. A nested cross-validation or a fully held-out test set must be used, with hyperparameters chosen using training folds only.
  2. [Table II] For each subject, the reported mean accuracies overlap within one standard deviation (MonC: 54.15±1.15 vs 53.43±0.55; MonJ: 35.98±1.15 vs 34.22±0.78). No statistical significance test is provided, and with only five folds the variance estimates are noisy. The authors should report per-fold results and apply a paired test across folds (or across repeated runs) to support the claim that the accuracy difference is not due to chance.
  3. [Table III and Section IV-C] Table III does not support the claims made in the text. With the 2D encoder, the bi-band 512+32 model achieves 46.04%, which is lower than both the 512-only model (46.13%) and the 32-only model (46.27%), contradicting the statement in Section IV-B that mixed TCNs improve performance. The gain to 46.77% in the last row is confounded by simultaneously switching to the 3D encoder. Furthermore, Section IV-C claims the 3D encoder improves accuracy by 1.04%, but the last two rows of Table III differ by only 0.73 percentage points, and the conclusion cites 0.5%. These numbers must be reconciled, and the bi-band versus solo-band comparison must be conducted with the encoder type held fixed.
  4. [Algorithm 1 and Figure 7] The frequency importance test applies 5 Hz band-pass filters to the input ECoG and feeds the filtered signals to a pretrained model. Because the model was trained on full-spectrum data, lower accuracy on filtered inputs reflects distribution shift rather than a causal measure of the frequency bands' contribution to the decision. The strong conclusions in Section V (item 1) — that distinguishing information comes only from the low-frequency region and that the high-frequency domain contains almost no effective information — are therefore not established. These results should be treated as exploratory, or validated by training a model on filtered data.
minor comments (6)
  1. [Sections I and IV-C] Reference '[0]' is undefined; the citations to the baseline architecture should point to [1] or another appropriate reference.
  2. [Table I] 'nn.Cov3d' should be 'nn.Conv3d' throughout the table.
  3. [Section III-C] References [2] and [11] are the same paper, as are [13] and [15]; please deduplicate.
  4. [Section III-C] The Adam optimizer is cited as [32], which is Gross's 'Inferior temporal cortex' entry; the original Adam paper (Kingma and Ba, 2015) or another standard source should be cited instead.
  5. [Table II] The table header is garbled, with repeated 'MonJ' labels and unclear column grouping; please reformat the table so that columns for subject, model size, accuracy, and speed are clearly labeled.
  6. [Algorithm 1] There are typos in Algorithm 1: 'f ron' and 'f rof f' should be 'from' and 'off'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the accuracy and speed claims are empirical comparisons, not derivations that reduce to fitted inputs.

full rationale

The paper's central claims are (i) Bi-BCWT replaces MST with learned 1D convolutions, (ii) the 2D spatial encoder improves accuracy, and (iii) the full model is smaller, faster, and 1.24% more accurate than the author's prior MST-ECoGNet. Each of these is supported by measured experiments (Table II, Figures 5-7, Table III) against baselines that are not equivalent by construction. No equation defines X in terms of Y, no fitted parameter is renamed as a prediction, and no uniqueness theorem is imported from self-citation. The only concern is an experimental-design one: kernel sizes and TCN count were selected on the same 5-fold CV used to report final accuracy (Section IV-A), which may inflate the reported gain, but this is a selection-bias/correctness risk rather than circularity, since the accuracy numbers are measured, not derived from the selection criterion. Self-citation to [1] is used as a baseline and motivation, but the performance comparison is empirical and no load-bearing argument reduces to the citation itself. Hence no circular step is identified.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper is an empirical deep learning study; every reported number is the outcome of training on a private dataset. The central accuracy claim rests on hyperparameters (kernel lengths, TCN count, learning rate) chosen partly by performance on the same data, and on domain assumptions about the electrode-array geometry and the normalization procedure.

free parameters (4)
  • convolution kernel lengths (512 and 32) = 512 and 32
    Selected based on accuracy comparisons on the same dataset (Section IV-A, Figure 6). The two lengths are claimed to cover low and high frequency ranges.
  • number of TCN modules (64) = 64
    Accuracy increases with TCN count (Figure 5); 64 was chosen as the largest tested value, effectively a tuned hyperparameter.
  • learning rate = 1.5e-6
    Reported in Section III-C without justification or a schedule; this unusually small value appears to be hand-chosen for this dataset.
  • spatial encoder kernel sizes = 8x8x1 and 1x2x1
    Chosen to compress the 8x16 electrode grid to 1x1; no ablation over these sizes is shown.
assumptions (4)
  • domain assumption Convolution kernels with learnable parameters can replace a hand-designed time-frequency transform (MST) without loss of discriminative information.
    Section II-A states the Bi-BCWT module 'has similar functions to MST' based on kernel size tuning; this equivalence is assumed, not proven.
  • domain assumption The 8x16 electrode array geometry on the inferior temporal cortex provides meaningful 2D spatial structure that a 2D convolution can exploit.
    Section II-B assumes the physical electrode layout maps directly to 2D spatial filters; the ablation (Table III) shows a 1.04% gain without confidence intervals.
  • domain assumption Normalizing each trial by the pre-stimulus background mean and standard deviation removes non-task-related state effects.
    Section III-B states this normalization 'exclude[s] the influence of the monkey's state'; the validity of this stationarity assumption is not tested.
  • ad hoc to paper Filtering the raw ECoG into 5 Hz bands and measuring the pretrained model's accuracy on those filtered inputs reveals which frequency bands carry task information.
    Algorithm 1 uses the same trained model to judge frequency bands; this is a post-hoc explanation, not an independent probe.

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Cite this review

Pith. "Pith review of Bi-Band ECoGNet for ECoG Decoding on Classification Task." pith.science (2026). https://pith.science/paper/SGE5XALV

@misc{pith2026241200378,
  author       = {Pith},
  title        = {Pith review of: Bi-Band ECoGNet for ECoG Decoding on Classification Task},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SGE5XALV}},
  note         = {Machine review of arXiv:2412.00378}
}
read the original abstract

In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accuracy is one of the current research hotspots. ECoG acquisition uses a high-density electrode array and a high sampling frequency, which makes ECoG data have a certain high similarity and data redundancy in the temporal domain, and also unique spatial pattern in spatial domain. How to effectively extract features is both exciting and challenging. Previous work found that visual-related ECoG can carry visual information via frequency and spatial domain. Based on this finding, we focused on using deep learning to design frequency and spatial feature extraction modules, and proposed a Bi-Band ECoGNet model based on deep learning. The main contributions of this paper are: 1) The Bi-BCWT (Bi-Band Channel-Wise Transform) neural network module is designed to replace the time-consume method MST, this module greatly improves the model calculation and data storage efficiency, and effectively increases the training speed; 2) The Bi-BCWT module can effectively take into account the information both in low-frequency and high-frequency domain, which is more conducive to ECoG multi-classification tasks; 3) ECoG is acquired using 2D electrode array, the newly designed 2D Spatial-Temporal feature encoder can extract the 2D spatial feature better. Experiments have shown that the unique 2D spatial data structure can effectively improve classification accuracy; 3) Compared with previous work, the Bi-Band ECoGNet model is smaller and has higher performance, with an accuracy increase of 1.24%, and the model training speed is increased by 6 times, which is more suitable for BCI applications.

Figures

Figures reproduced from arXiv: 2412.00378 by the authors.

Figure 1
Figure 1. Outline of Data Flow of Bi-Band ECoGNet [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 4
Figure 4. Outline of ECoG Record Each image will be used as visual stimuli, last 300 ms, ECoG signal will be measured via electrode array. B. ECoG Record Preprocess Each ECoG trail contains 3 parts, one is an active interval that lasts for 300 milliseconds. In this interval, picture is displayed to stimulate the monkey’s visual system; the other two are the pre-static interval and the post-static interval, picture is not disp… view at source ↗
Figure 5
Figure 5. Performance of Different Kernel Number Results show that the more kernel (TCN), the better performance [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figures from the paper (5 more)
Figure 6
Figure 6. Figure 6: Performance of Different Size of Kernel The size (length) of kernel has influence on performance. Finally 32 & 512 kernel size are selected, base on the performance. To prove this conjecture, we designed a verification experi￾ment: designing an FIR band-pass digital fi…
Figure 7
Figure 7. Figure 7: Frequency Importance of Different Kernel Size Results show that model with big size of kernel such as 512, main focus on low frequency information, small size kernel like 32 can get information from relatively high frequency. However, when combine these two kernel size…
Figure 8
Figure 8. Figure 8: Spatial-Temporal Feature Encoder of 2DECoGNet Each Spatial-Temporal feature map will processed by 1D convolution kernel, then get feature vector, the big difference compare with 3DECoGNet is that no reshape operation on the feature map. The comparison results are shown…
Figure 9
Figure 9. Figure 9: We can find that the heat region of 3DECoGNet [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Heat Map of MonC & MonJ Dataset Results show that there is big difference in channel heat map between MonC & MonJ dataset.The position where visual information comes from is quite different between 2 subjects. V. CONCLUSION There are very few studies on ECoG signals g…

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    cs.LG 2026-02 reject novelty 5.0 of 10

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Reference graph

Works this paper leans on

34 extracted references · 31 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

    " write newline "" initialize.prev.this.status FUNCTION begin.bib " write newline preamble empty 'skip preamble write newline if " thebibliography " longest.label * " " * write newline " [1] #1 " write newline " url@samestyle " write newline " " write newline " [2] #2 " write newline " =0pt " write newline " " ALTinterwordstretchfactor * " " * write newli...

  2. [2]

    11em plus .33em minus .07em 4000 4000 100 4000 4000 500 `\.=1000 = #1 \@IEEEnotcompsoconly \@IEEEcompsoconly #1 * [1] 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEcompsocnotconfonly \@IEEEcompsocconfonly \@IEEEauthorblockAstyle \@IEEEcompsocnotconfonly \@IEEEcompsocconfonly \@IEEEcompsocconfonly \@IEEEauthordefaulttextstyle \@IEEEcompsocnotc...

  3. [3]

    Explainable MST-ECoGNet Decode Visual Information from ECoG Signal

    JI, C., ``Explainable mst-ecognet decode visual information from ecog signal,'' 2024. [Online]. Available: https://arxiv.org/abs/2411.16165

    work page Pith review arXiv 2024

  4. [4]

    Jiao, B., Rui, Y., Gao, M., Fei, H., and Yu, Q., ``A modified s transform with adjustable window function,'' in 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), 2019, pp. 1--5

    work page 2019

  5. [5]

    Romanelli, P., Piangerelli, M., Ratel, D., Gaude, C., Costecalde, T., Puttilli, C., Picciafuoco, M., Benabid, A., and Torres, N., ``A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface,'' Journal of Neurosurgery, vol. 130, no. 4, pp. 1166 -- 1179, 2019. [Online]. Av...

    work page 2019

  6. [6]

    Wakuya, M., Inoue, T., Imoto, H., Maruta, Y., Nomura, S., Suzuki, M., and Yamakawa, T., ``Epileptic seizure–related changes in electrocorticogram, cortical temperature, and cerebral hemodynamics obtained via an implantable multimodal multichannel probe during preoperative monitoring: illustrative case,'' Journal of Neurosurgery: Case Lessons, vol. 3, no. ...

    work page 2022

  7. [7]

    R., Zhao, X., Miao, Y., Sugano, H., and Tanaka, T., ``Epileptic seizure focus detection from interictal electroencephalogram: a survey,'' Cognitive Neurodynamics, vol

    Islam, M. R., Zhao, X., Miao, Y., Sugano, H., and Tanaka, T., ``Epileptic seizure focus detection from interictal electroencephalogram: a survey,'' Cognitive Neurodynamics, vol. 17, p. 1, 2 2023. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871145/

    work page 2023

  8. [8]

    L., Schiff, N

    Yao, L., Baker, J. L., Schiff, N. D., Purpura, K. P., and Shoaran, M., ``Predicting task performance from biomarkers of mental fatigue in global brain activity,'' Journal of Neural Engineering, vol. 18, no. 3, p. 036001, mar 2021. [Online]. Available: https://dx.doi.org/10.1088/1741-2552/abc529

    work page doi:10.1088/1741-2552/abc529 2021

Show all 34 references
  1. [9]

    L., Roberts, D., Splinter, W

    Keene, D. L., Roberts, D., Splinter, W. M., Higgins, M., and Ventureyra, E., ``Alfentanil mediated activation of epileptiform activity in the electrocorticogram during resection of epileptogenic foci,'' Can. J. Neurol. Sci, vol. 24, pp. 37--39, 1997. [Online]. Available: https...

    1997 doi

  2. [10]

    Zhou, F. X. W. Z. D. S. Q. Y. W., ``Decoding spectro-temporal representation for motor imagery recognition using ecog-based brain-computer interfaces,'' JIN, vol. 19, no. 2, pp. 259--272, 2020

    2020

  3. [11]

    and Bougrain, L., ``Decoding finger flexion from band-specific ecog signals in humans,'' Frontiers in Neuroscience, vol

    Liang, N. and Bougrain, L., ``Decoding finger flexion from band-specific ecog signals in humans,'' Frontiers in Neuroscience, vol. 6, 2012. [Online]. Available: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2012.00091

    2012 arXiv

  4. [12]

    Thangaraj, K., Muruganandham, J., Selvaumar, S., and Jagan, R., ``Analysis of harmonics using s-transform,'' in 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), 2016, pp. 1--5

    2016

  5. [13]

    and Boashash, B., ``Evaluation of the modified s-transform for time-frequency synchrony analysis and source localisation,'' eurasipjournals, 2012

    Assous, S. and Boashash, B., ``Evaluation of the modified s-transform for time-frequency synchrony analysis and source localisation,'' eurasipjournals, 2012. [Online]. Available: http://asp.eurasipjournals.com/content/2012/1/49

    2012

  6. [14]

    2, 2010, pp

    Zhao, H.-b., Yu, C.-y., Liu, C., and Wang, H., ``Ecog-based brain-computer interface using relative wavelet energy and probabilistic neural network,'' in 2010 3rd International Conference on Biomedical Engineering and Informatics, vol. 2, 2010, pp. 873--877

    2010

  7. [15]

    CSAE '21

    Shi, H., Yu, P., and Li, H., ``The finger flexion related feature extraction method based on wavelet time-frequency analysis in ecog signals,'' in Proceedings of the 5th International Conference on Computer Science and Application Engineering, ser. CSAE '21. 1em plus 0.5em min...

    2021

  8. [16]

    Hammon, P. S. and de Sa, V. R., ``Preprocessing and meta-classification for brain-computer interfaces,'' IEEE Transactions on Biomedical Engineering, vol. 54, no. 3, pp. 518--525, 2007

    2007

  9. [17]

    Xu, F., Zhou, W., Zhen, Y., Yuan, Q., and Wu, Q., ``Using fractal and local binary pattern features for classification of ecog motor imagery tasks obtained from the right brain hemisphere,'' International Journal of Neural Systems, vol. 26, no. 06, p. 1650022, 2016, pMID: 2725...

    2016 doi

  10. [18]

    and Shoaran, M., ``Enhanced classification of individual finger movements with ecog,'' in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp

    Yao, L. and Shoaran, M., ``Enhanced classification of individual finger movements with ecog,'' in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 2063--2066

    2019

  11. [19]

    Jiang, T., Jiang, T., Wang, T., Mei, S., Liu, Q., Li, Y., Wang, X., Prabhu, S., Sha, Z., and Ince, N. F., ``Characterization and decoding the spatial patterns of hand extension/flexion using high-density ecog,'' IEEE Transactions on Neural Systems and Rehabilitation Engineerin...

    2017

  12. [20]

    Li, Y., Koike, Y., and Sugiyama, M., ``A framework of adaptive brain computer interfaces,'' in 2009 2nd International Conference on Biomedical Engineering and Informatics, 2009, pp. 1--5

    2009

  13. [21]

    Deng, X., Li, D., Mi, J., Gao, F., Chen, Q., Wang, J., and Liu, R., ``Motor imagery ecog signal classification using sparse representation with elastic net constraint,'' in 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 2018, pp. 44--49

    2018

  14. [22]

    3, 2010, pp

    Chong, L., Hai-bin, Z., Chun-sheng, L., and Hong, W., ``Classification of ecog signals for motor imagery tasks,'' in 2010 2nd International Conference on Signal Processing Systems, vol. 3, 2010, pp. V3--185--V3--188

    2010

  15. [23]

    Saa, J. F. D., de Pesters, A., and Cetin, M., ``Asynchronous decoding of finger movements from ecog signals using long-range dependencies conditional random fields,'' Journal of Neural Engineering, vol. 13, no. 3, p. 036017, may 2016. [Online]. Available: https://dx.doi.org/10...

    2016 doi

  16. [24]

    [Online]

    Jain, R., Jaiman, P., and Baths, V., ``Feature engineering for an efficient motor related ecog bci system,'' bioRxiv, 2023. [Online]. Available: https://www.biorxiv.org/content/early/2023/04/15/2023.04.01.535201

    2023

  17. [25]

    Date, H., Kawasaki, K., Hasegawa, I., and Okatani, T., ``Deep learning for channel-agnostic brain decoding across multiple subjects,'' in 2020 8th International Winter Conference on Brain-Computer Interface (BCI), 2020, pp. 1--6

    2020

  18. [26]

    and Zahid, U., ``Long short-term memory recurrent neural network architectures for urdu acoustic modeling,'' Int

    Zia, T. and Zahid, U., ``Long short-term memory recurrent neural network architectures for urdu acoustic modeling,'' Int. J. Speech Technol., vol. 22, no. 1, p. 21–30, mar 2019. [Online]. Available: https://doi.org/10.1007/s10772-018-09573-7

    2019 doi

  19. [27]

    0430--0435, 2018

    Du, A., Yang, S., Liu, W., and Huang, H., ``Decoding ecog signal with deep learning model based on lstm,'' TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 0430--0435, 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:67874163

    2018

  20. [28]

    Śliwowski, M., Martin, M., Souloumiac, A., Blanchart, P., and Aksenova, T., ``Decoding ecog signal into 3d hand translation using deep learning,'' Journal of Neural Engineering, vol. 19, no. 2, p. 026023, mar 2022. [Online]. Available: https://dx.doi.org/10.1088/1741-2552/ac5d69

    2022 doi

  21. [29]

    and Paliwal, K., ``Bidirectional recurrent neural networks,'' IEEE Transactions on Signal Processing, vol

    Schuster, M. and Paliwal, K., ``Bidirectional recurrent neural networks,'' IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2673--2681, 1997

    1997

  22. [30]

    J., Solon, A

    Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., and Lance, B. J., ``Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces,'' Journal of Neural Engineering, vol. 15, no. 5, p. 056013, jul 2018. [Online]. Available: ht...

    2018 doi

  23. [31]

    2331--2336

    Date, H., Kawasaki, K., Hasegawa, I., and Okatani, T., ``Deep learning for natural image reconstruction from electrocorticography signals,'' in 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019, pp. 2331--2336

    2019

  24. [32]

    G., ``Inferior temporal cortex,'' Scholarpedia, vol

    Gross, C. G., ``Inferior temporal cortex,'' Scholarpedia, vol. 3, p. 7294, 12 2008

    2008

  25. [33]

    Wei, Q. and Tu, W., ``Channel selection by genetic algorithms for classifying single-trial ecog during motor imagery,'' in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 624--627

    2008

  26. [34]

    Y, L. X. W. D. Z. B. F. C. C. J. X. M. C., ``A review on electroencephalogram based channel selection,'' Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, vol. 41, no. 2, p. 154, apr 2024

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.