Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems (2402.09448v1)

Published 31 Jan 2024 in eess.SP, cs.AI, and cs.LG

Abstract: This study aims to enhance BCI applications for individuals with motor impairments by comparing the effectiveness of tripolar EEG (tEEG) with conventional EEG. The focus is on interpreting and decoding various grasping movements, such as power grasp and precision grasp. The goal is to determine which EEG technology is more effective in processing and translating grasp related neural signals. The approach involved experimenting on ten healthy participants who performed two distinct grasp movements: power grasp and precision grasp, with a no movement condition serving as the baseline. Our research presents a thorough comparison between EEG and tEEG in decoding grasping movements. This comparison spans several key parameters, including signal to noise ratio (SNR), spatial resolution via functional connectivity, ERPs, and wavelet time frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated superior performance over conventional EEG in various aspects. This included a higher signal to noise ratio, enhanced spatial resolution, and more informative data in ERPs and wavelet time frequency analysis. The use of tEEG led to notable improvements in decoding accuracy for differentiating movement types. Specifically, tEEG achieved around 90% accuracy in binary and 75.97% for multiclass classification. These results are markedly better than those from standard EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively. These findings highlight the superior effectiveness of tEEG over EEG in decoding grasp types and its competitive or superior performance in complex classifications compared with existing research.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. Jonathan R Wolpaw and Dennis J McFarland “Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans” In Proceedings of the national academy of sciences 101.51 National Acad Sciences, 2004, pp. 17849–17854
  2. “BCI2000: a general-purpose brain-computer interface (BCI) system” In IEEE Transactions on biomedical engineering 51.6 IEEE, 2004, pp. 1034–1043
  3. “A comprehensive review of EEG-based brain–computer interface paradigms” In Journal of neural engineering 16.1 IOP Publishing, 2019, pp. 011001
  4. “The cortical control of visually guided grasping” In The Neuroscientist 14.2 Sage Publications Sage CA: Los Angeles, CA, 2008, pp. 157–170
  5. Scott T Grafton “The cognitive neuroscience of prehension: recent developments” In Experimental brain research 204 Springer, 2010, pp. 475–491
  6. “Decoding the neural mechanisms of human tool use” In elife 2 eLife Sciences Publications, Ltd, 2013, pp. e00425
  7. Umberto Castiello “The neuroscience of grasping” In Nature Reviews Neuroscience 6.9 Nature Publishing Group UK London, 2005, pp. 726–736
  8. “A rehabilitation device to improve the hand grasp function of stroke patients using a patient-driven approach” In 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), 2013, pp. 1–4 IEEE
  9. Rui CV Loureiro and William S Harwin “Reach & grasp therapy: design and control of a 9-DOF robotic neuro-rehabilitation system” In 2007 IEEE 10th International Conference on Rehabilitation Robotics, 2007, pp. 757–763 IEEE
  10. “Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob” In 2009 IEEE International Conference on Rehabilitation Robotics, 2009, pp. 22–27 IEEE
  11. “Rehabilitation of reaching and grasping function in severe hemiplegic patients using functional electrical stimulation therapy” In Neurorehabilitation and neural repair 22.6 SAGE Publications Sage CA: Los Angeles, CA, 2008, pp. 706–714
  12. “Closed-loop neuroprosthesis for reach-to-grasp assistance: combining adaptive multi-channel neuromuscular stimulation with a multi-joint arm exoskeleton” In Frontiers in neuroscience 10 Frontiers Media SA, 2016, pp. 284
  13. “Brain–computer interfaces and assistive technology” In Brain-Computer-Interfaces in their ethical, social and cultural contexts Springer, 2014, pp. 7–38
  14. “Combining brain–computer interfaces and assistive technologies: state-of-the-art and challenges” In Frontiers in neuroscience Frontiers, 2010, pp. 161
  15. Mikhail A Lebedev and Miguel AL Nicolelis “Brain–machine interfaces: past, present and future” In TRENDS in Neurosciences 29.9 Elsevier, 2006, pp. 536–546
  16. Mehrnaz Kh Hazrati and Abbas Erfanian “An online EEG-based brain–computer interface for controlling hand grasp using an adaptive probabilistic neural network” In Medical engineering & physics 32.7 Elsevier, 2010, pp. 730–739
  17. Yuchou Chang “Architecture design for performing grasp-and-lift tasks in brain–machine-interface-based human-in-the-loop robotic system” In IET Cyber-Physical Systems: Theory & Applications 4.3 Wiley Online Library, 2019, pp. 198–203
  18. “Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements” In Journal of neurophysiology 79.1 American Physiological Society Bethesda, MD, 1998, pp. 159–173
  19. “Object vision to hand action in macaque parietal, premotor, and motor cortices” In elife 5 eLife Sciences Publications, Ltd, 2016, pp. e15278
  20. Jonathan A Michaels and Hansjörg Scherberger “Population coding of grasp and laterality-related information in the macaque fronto-parietal network” In Scientific reports 8.1 Nature Publishing Group UK London, 2018, pp. 1710
  21. “Power modulations of ECoG alpha/beta and gamma bands correlate with time-derivative of force during hand grasp” In Frontiers in neuroscience 14 Frontiers Media SA, 2020, pp. 100
  22. “Disentangling representations of object and grasp properties in the human brain” In Journal of Neuroscience 36.29 Soc Neuroscience, 2016, pp. 7648–7662
  23. “Neural representations of haptic object size in the human brain revealed by multivoxel fMRI patterns” In Journal of neurophysiology 124.1 American Physiological Society Bethesda, MD, 2020, pp. 218–231
  24. “A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications” In Journal of Big Data 10.1 Springer, 2023, pp. 46
  25. “Neural decoding of EEG signals with machine learning: A systematic review” In Brain Sciences 11.11 MDPI, 2021, pp. 1525
  26. “Human EEG reveals distinct neural correlates of power and precision grasping types” In NeuroImage 181 Elsevier, 2018, pp. 635–644
  27. “Decoding natural reach-and-grasp actions from human EEG” In Journal of neural engineering 15.1 IOP Publishing, 2017, pp. 016005
  28. “Decoding different reach-and-grasp movements using noninvasive electroencephalogram” In Frontiers in Neuroscience 15 Frontiers Media SA, 2021, pp. 684547
  29. “A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging” In Electroencephalography and clinical neurophysiology 90.1 Elsevier, 1994, pp. 40–57
  30. “Tri-polar concentric ring electrode development for Laplacian electroencephalography” In IEEE transactions on biomedical engineering 53.5 IEEE, 2006, pp. 926–933
  31. Kanthaiah Koka and Walter G Besio “Improvement of spatial selectivity and decrease of mutual information of tri-polar concentric ring electrodes” In Journal of neuroscience methods 165.2 Elsevier, 2007, pp. 216–222
  32. “Tripolar concentric EEG electrodes reduce noise” In Clinical Neurophysiology 131.1 Elsevier, 2020, pp. 193–198
  33. “Source localization of high-frequency activity in tripolar electroencephalography of patients with epilepsy” In Epilepsy & Behavior 101 Elsevier, 2019, pp. 106519
  34. “Recent advances in high-frequency oscillations and seizure onset detection using laplacian electroencephalography via tripolar concentric ring electrodes” In Proceedings 2.3, 2017 MDPI
  35. Saleh I Alzahrani and Charles W Anderson “A comparison of conventional and tri-polar eeg electrodes for decoding real and imaginary finger movements from one hand” In International Journal of Neural Systems 31.09 World Scientific, 2021, pp. 2150036
  36. Guy Vingerhoets “Contribution of the posterior parietal cortex in reaching, grasping, and using objects and tools” In Frontiers in psychology 5 Frontiers Media SA, 2014, pp. 151
  37. “Emulating conventional disc electrode with the outer ring of the tripolar concentric ring electrode in phantom and human electroencephalogram data” In 2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2013, pp. 1–4 IEEE
  38. “MEG and EEG data analysis with MNE-Python” In Frontiers in neuroscience Frontiers, 2013, pp. 267
  39. “Filters: when, why, and how (not) to use them” In Neuron 102.2 Elsevier, 2019, pp. 280–293
  40. Andreas Widmann, Erich Schröger and Burkhard Maess “Digital filter design for electrophysiological data–a practical approach” In Journal of neuroscience methods 250 Elsevier, 2015, pp. 34–46
  41. “Filter effects and filter artifacts in the analysis of electrophysiological data” In Frontiers in psychology 3 Frontiers Research Foundation, 2012, pp. 233
  42. Rene A Carmona and Lonnie H Hudgins “Wavelet denoising of EEG signals and identification of evoked response potentials” In Wavelet Applications in Signal and Image Processing II 2303, 1994, pp. 91–104 SPIE
  43. Ingrid Daubechies “Ten lectures on wavelets” SIAM, 1992
  44. Md Mamun, Mahmoud Al-Kadi and Mohd Marufuzzaman “Effectiveness of wavelet denoising on electroencephalogram signals” In Journal of applied research and technology 11.1 UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico, 2013, pp. 156–160
  45. “A study on discrete wavelet-based noise removal from EEG signals” In advances in computational biology, 2010, pp. 593–599 Springer
  46. “Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data” In NeuroImage 172 Elsevier, 2018, pp. 903–912
  47. “Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models” In Frontiers in neuroinformatics 15 Frontiers, 2021, pp. 777977
  48. “Independent component analysis: algorithms and applications” In Neural networks 13.4-5 Elsevier, 2000, pp. 411–430
  49. Anthony J Bell and Terrence J Sejnowski “An information-maximization approach to blind separation and blind deconvolution” In Neural computation 7.6 MIT Press, 1995, pp. 1129–1159
  50. “Progress in brain computer interface: Challenges and opportunities” In Frontiers in Systems Neuroscience 15 Frontiers Media SA, 2021, pp. 578875
  51. Avid Roman-Gonzalez “EEG signal processing for BCI applications” In Human–computer systems interaction: backgrounds and applications 2 Springer, 2012, pp. 571–591
  52. “Decoding Reach-to-Grasp Movements: A Comparative study of TCRE and EEG for High-Performance BCI Systems” In society for neuroscience, 2023

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets