Progress and translational challenges of brain-computer interfaces in the treatment of drug resistant epilepsy_Journal of Epilepsy

Authors：

ZHANG Juntao ^1,2 , LIU Anru ^1,2 , WAN Lijun ^1,2 , HAN Tao ^1,2 ,  LIU Xuewu ^1,2

1. Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250022, China;
2. Epilepsy Research Institute of Shandong University, Jinan 250022, China;

Corresponding?author：

LIU Xuewu, Email: snlxw1966@163.com

Keywords：

Brain computer interface; Epilepsy; Drug resistant epilepsy; Neuromodulation

DOI：

10.7507/2096-0247.202601002

Video：

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Abstract Full text Figures/Tables Video References Cited by

Epilepsy is a common chronic neurological disorder. Drug-resistant epilepsy (DRE) is defined as failure to achieve sustained seizure freedom after adequate trials of two appropriately chosen and tolerated anti-seizure medications (ASMs) regimens (as monotherapies or in combination). In this population, surgical resection and neuromodulation can effectively reduce seizure burden; however, their effectiveness is limited by uncertainty in epileptogenic zone localization, interindividual variability in stimulation targets and parameters, and procedure-related risks. As an emerging technology, brain–computer interface (BCI) offer a closed-loop framework of real-time monitoring, state decoding, decision-making, and intervention delivery, shifting therapy from fixed-parameter open-loop approaches to biomarker-driven, dynamically personalized modulation and providing new avenues for precision, individualized treatment of DRE. This review summarizes signal acquisition and decoding strategies for epilepsy BCI, closed-loop intervention paradigms, and key challenges in clinical translation.

Citation： ZHANG Juntao, LIU Anru, WAN Lijun, HAN Tao, LIU Xuewu. Progress and translational challenges of brain-computer interfaces in the treatment of drug resistant epilepsy. Journal of Epilepsy, 2026, 12(2): 152-158. doi: 10.7507/2096-0247.202601002 Copy

1.	Kwan P, Arzimanoglou A, Berg AT, et al. Definition of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia, 2010, 51(6): 1069-1077.
2.	Monteagudo-gimeno E, Sánchez-gonzález R, Raduà-casta?o J, et al. Association between depressive and anxious symptoms with cognitive function and quality of life in drug-resistant epilepsy. Heliyon, 2023, 9(10): e20903.
3.	Yardi R, Morita-sherman ME, Fitzgerald Z, et al. Long-term outcomes of reoperations in epilepsy surgery. Epilepsia, 2020, 61(3): 465-478.
4.	Touma L, Dansereau B, Chan AY, et al. Neurostimulation in people with drug-resistant epilepsy: Systematic review and meta-analysis from the ILAE Surgical Therapies Commission. Epilepsia, 2022, 63(6): 1314-1329.
5.	Papadelis C, Perry MS. Localizing the epileptogenic zone with novel biomarkers. Semin Pediatr Neurol, 2021, 39: 100919.
6.	Weber PB, Kapur R, Gwinn RP, et al. Infection and erosion rates in trials of a cranially implanted neurostimulator do not increase with subsequent neurostimulator placements. Stereotact Funct Neurosurg, 2017, 95(5): 325-329.
7.	Zou JJ, Chen HMJ, Chen XY, et al. Noninvasive closed-loop acoustic brain-computer interface for seizure control. Theranostics, 2024, 14(15): 5965-5981.
8.	Peksa J, Mamchur D. State-of-the-art on brain-computer interface technology. Sensors, 2023, 23(13): 6001.
9.	Zhao ZP, Nie C, Jiang CT, et al. Modulating brain activity with invasive brain-computer interface: a narrative review. Brain Sci, 2023, 13(1): 134.
10.	Khoo HM, Hall JA, Dubeau F, et al. Technical aspects of SEEG and its interpretation in the delineation of the epileptogenic zone. Neurol Med-chir, 2020, 60(12): 565-580.
11.	Merk T, K?hler R, Peterson V, et al. Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants. Res Sq, 2023.
12.	Zhang HY, Jiao L, Yang SX, et al. Brain-computer interfaces: the innovative key to unlocking neurological conditions. Int J Surg, 2024, 110(9): 5745-5762.
13.	Liu XY, Wang WL, Liu M, et al. Recent applications of EEG-based brain-computer-interface in the medical field. Military Med Res, 2025, 12(1): 14.
14.	Edelman BJ, Zhang SL, Schalk G, et al. Non-invasive brain-computer interfaces: state of the art and trends. Ieee Rev Biomed Eng, 2025, 18: 26-49.
15.	Alkawadri R. Brain-computer interface (BCI) applications in mapping of epileptic brain networks based on intracranial-EEG: an update. Front Neurosci, 2019, 13: 191.
16.	Mridha MF, Das SC, Kabir MM, et al. Brain-computer interface: advancement and challenges. Sensors, 2021, 21(17): 5746.
17.	Light G A, Williams L E, Minow F, et al. Electroencephalography (EEG) and event-related potentials (ERPs) with human participants. Curr Protoc Neurosci, 2010, 6: 1-24.
18.	Wu XL, Li GY, Jiang SZ, et al. Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings. J Neural Eng, 2022, 19(2): 026047.
19.	Hnazaee MF, Wittevrongel B, Khachatryan E, et al. Localization of deep brain activity with scalp and subdural EEG. Neuroimage, 2020, 223: 117344.
20.	Bu YF, Harrington DL, Lee RR, et al. Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning. Cereb Cortex, 2023, 33(14): 8942-8955.
21.	Ahlfors SP, Mody M. Overview of MEG. Organ Res Methods, 2019, 22(1): 95-115.
22.	Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia, 2019, 60(10): 2037-2047.
23.	Boonyakitanont P, Lek-uthai A, Chomtho K, et al. A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed Signal Process Control, 2020, 57: 101702.
24.	Hossain KM, Islam MA, Hossain S, et al. Status of deep learning for EEG-based brain-computer interface applications. Front Comput Neurosci, 2023, 16: 1006763.
25.	Wang XS, Wang YL, Liu DW, et al. Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM. Sci Rep, 2023, 13(1): 14876.
26.	Toprani S, Durand DM. Mechanisms of neurostimulation for epilepsy. Epilepsy Curr, 2023, 23(5): 298-302.
27.	Lo MC, Widge AS. Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness. Int Rev Psych, 2017, 29(2): 191-204.
28.	Ortiz-guerrero G, Gregg NM. Biomarkers for epilepsy deep brain stimulation. J Clin Neurophysiol, 2025, 42(6): 486-492.
29.	Trevelyan AJ, Marks VS, Graham RT, et al. On brain stimulation in epilepsy. Brain, 2025, 148(3): 746-752.
30.	Gouveia FV, Warsi NM, Suresh H, et al. Neurostimulation treatments for epilepsy: Deep brain stimulation, responsive neurostimulation and vagus nerve stimulation. Neurotherapeutics, 2024, 21(3): e00308.
31.	Rao VR, Rolston JD. Unearthing the mechanisms of responsive neurostimulation for epilepsy. Communications Med, 2023, 3(1): 166.
32.	Chen Z, Liu KZ. Mechanism and applications of vagus nerve stimulation. Curr Issues Mol Biol, 2025, 47(2): 122.
33.	Ding YQ, Guo KL, Wang XJ, et al. Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy. Transl Neurosci, 2023, 14(1): 20220308.
34.	Perez-malagon CD, Lopez-gonzalez MA. Epilepsy and deep brain stimulation of anterior thalamic nucleus. Cureus J Med Sci, 2021, 13(9): e18199.
35.	Maksimenko VA, Van heukelum S, Makarov VV, et al. Absence seizure control by a brain computer interface. Sci Rep, 2017, 7: 2487.
36.	Campos-rodriguez C, Palmer D, Forcelli PA. Optogenetic stimulation of the superior colliculus suppresses genetic absence seizures. Brain, 2023, 146(10): 4320-4335.
37.	Shon YM, Park HR, Lee S. Deep brain stimulation therapy for drug-resistant epilepsy: present and future perspectives. J Epilepsy Res, 2025, 15(1): 33-41.
38.	Gummadavelli A, Zaveri HP, Spencer DD, et al. Expanding brain-computer interfaces for controlling epilepsy networks: novel thalamic responsive neurostimulation in refractory epilepsy. Front Neurosci, 2018, 12: 474.
39.	Neumann WJ, Gilron R, Little S, et al. Adaptive deep brain stimulation: from experimental evidence toward practical implementation. Mov Disord, 2023, 38(6): 937-948.
40.	Zou JJ, Meng L, Lin ZR, et al. Ultrasound neuromodulation inhibits seizures in acute epileptic monkeys. Iscience, 2020, 23(5): 101066.
41.	Beisteiner R, Hallett M, Lozano AM. Ultrasound neuromodulation as a new brain therapy. Adv Sci, 2023, 10(14): 15689.
42.	Nair DR, Laxer KD, Weber PB, et al. Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy. Neurology, 2020, 95(9): E1244-E1256.
43.	Li QH, Shan YZ, Wei PH, et al. The comparison of DBS and RNS for adult drug-resistant epilepsy: a systematic review and meta-analysis. Front Hum Neurosci, 2024, 18: 1429223.
44.	Salanova V, Sperling MR, Gross RE, et al. The SANTe study at 10 years of follow-up: effectiveness, safety, and sudden unexpected death in epilepsy. Epilepsia, 2021, 62(6): 1306-1317.
45.	Feldmann LK, Neumann WJ, Faust K, et al. Risk of infection after deep brain stimulation surgery with externalization and local-field potential recordings: twelve-year experience from a single institution. Stereotact Funct Neurosurg, 2021, 99(6): 512-520.
46.	Jung IH, Chang KW, Park SH, et al. Complications after deep brain stimulation: a 21-year experience in 426 patients. Front Aging Neurosci, 2022, 14: 819730.
47.	Becker LL, Gratopp A, Prager C, et al. Treatment of pediatric convulsive status epilepticus. Front Neurol, 2023, 14: 1175370.
48.	Costa G, Teixeira C, Pinto MF. Comparison between epileptic seizure prediction and forecasting based on machine learning. Sci Rep, 2024, 14(1): 5653.
49.	Van mierlo P, Vorderwülbecke BJ, Staljanssens W, et al. Ictal EEG source localization in focal epilepsy: review and future perspectives. Clin Neurophysiol, 2020, 131(11): 2600-2616.
50.	Vaziri AY, Makkiabadi B. Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization. Front Neurosci, 2025, 18: 1505017.
51.	Jung Y, Mithani K, Suresh H, et al. Deep brain stimulation in pediatric populations: a scoping review of the clinical trial landscape. Stereotact Funct Neurosurg, 2025, 103(2): 132-144.
52.	Bergeron D, Iorio-morin C, Bonizzato M, et al. Use of invasive brain-computer interfaces in pediatric neurosurgery: technical and ethical considerations. J Child Neurol, 2023, 38(3-4): 223-238.
53.	Bóné B, Kovács N, Balás I, et al. Pregnancy and deep brain stimulation therapy for epilepsy. Epileptic Disord, 2021, 23(4): 633-638.
54.	Ding J, Wang L, Wang C, et al. Is vagal-nerve stimulation safe during pregnancy? A mini review. Epilepsy Res, 2021, 174: 106671.
55.	Li Y, Eliashiv D, Lahue SC, et al. Pregnancy outcomes of refractory epilepsy patients treated with Brain-responsive neurostimulation. Epilepsy Res, 2021, 169: 106532.
56.	Meng LB, Jiang X, Huang J, et al. User identity protection in EEG-based brain–computer interfaces. Ieee Trans Neural Syst Rehabil Eng, 2023, 31: 3576-3586.
57.	Pernet CR, Appelhoff S, Gorgolewski KJ, et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci Data, 2019, 6: 103.

1. Kwan P, Arzimanoglou A, Berg AT, et al. Definition of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia, 2010, 51(6): 1069-1077.
2. Monteagudo-gimeno E, Sánchez-gonzález R, Raduà-casta?o J, et al. Association between depressive and anxious symptoms with cognitive function and quality of life in drug-resistant epilepsy. Heliyon, 2023, 9(10): e20903.
3. Yardi R, Morita-sherman ME, Fitzgerald Z, et al. Long-term outcomes of reoperations in epilepsy surgery. Epilepsia, 2020, 61(3): 465-478.
4. Touma L, Dansereau B, Chan AY, et al. Neurostimulation in people with drug-resistant epilepsy: Systematic review and meta-analysis from the ILAE Surgical Therapies Commission. Epilepsia, 2022, 63(6): 1314-1329.
5. Papadelis C, Perry MS. Localizing the epileptogenic zone with novel biomarkers. Semin Pediatr Neurol, 2021, 39: 100919.
6. Weber PB, Kapur R, Gwinn RP, et al. Infection and erosion rates in trials of a cranially implanted neurostimulator do not increase with subsequent neurostimulator placements. Stereotact Funct Neurosurg, 2017, 95(5): 325-329.
7. Zou JJ, Chen HMJ, Chen XY, et al. Noninvasive closed-loop acoustic brain-computer interface for seizure control. Theranostics, 2024, 14(15): 5965-5981.
8. Peksa J, Mamchur D. State-of-the-art on brain-computer interface technology. Sensors, 2023, 23(13): 6001.
9. Zhao ZP, Nie C, Jiang CT, et al. Modulating brain activity with invasive brain-computer interface: a narrative review. Brain Sci, 2023, 13(1): 134.
10. Khoo HM, Hall JA, Dubeau F, et al. Technical aspects of SEEG and its interpretation in the delineation of the epileptogenic zone. Neurol Med-chir, 2020, 60(12): 565-580.
11. Merk T, K?hler R, Peterson V, et al. Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants. Res Sq, 2023.
12. Zhang HY, Jiao L, Yang SX, et al. Brain-computer interfaces: the innovative key to unlocking neurological conditions. Int J Surg, 2024, 110(9): 5745-5762.
13. Liu XY, Wang WL, Liu M, et al. Recent applications of EEG-based brain-computer-interface in the medical field. Military Med Res, 2025, 12(1): 14.
14. Edelman BJ, Zhang SL, Schalk G, et al. Non-invasive brain-computer interfaces: state of the art and trends. Ieee Rev Biomed Eng, 2025, 18: 26-49.
15. Alkawadri R. Brain-computer interface (BCI) applications in mapping of epileptic brain networks based on intracranial-EEG: an update. Front Neurosci, 2019, 13: 191.
16. Mridha MF, Das SC, Kabir MM, et al. Brain-computer interface: advancement and challenges. Sensors, 2021, 21(17): 5746.
17. Light G A, Williams L E, Minow F, et al. Electroencephalography (EEG) and event-related potentials (ERPs) with human participants. Curr Protoc Neurosci, 2010, 6: 1-24.
18. Wu XL, Li GY, Jiang SZ, et al. Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings. J Neural Eng, 2022, 19(2): 026047.
19. Hnazaee MF, Wittevrongel B, Khachatryan E, et al. Localization of deep brain activity with scalp and subdural EEG. Neuroimage, 2020, 223: 117344.
20. Bu YF, Harrington DL, Lee RR, et al. Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning. Cereb Cortex, 2023, 33(14): 8942-8955.
21. Ahlfors SP, Mody M. Overview of MEG. Organ Res Methods, 2019, 22(1): 95-115.
22. Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia, 2019, 60(10): 2037-2047.
23. Boonyakitanont P, Lek-uthai A, Chomtho K, et al. A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed Signal Process Control, 2020, 57: 101702.
24. Hossain KM, Islam MA, Hossain S, et al. Status of deep learning for EEG-based brain-computer interface applications. Front Comput Neurosci, 2023, 16: 1006763.
25. Wang XS, Wang YL, Liu DW, et al. Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM. Sci Rep, 2023, 13(1): 14876.
26. Toprani S, Durand DM. Mechanisms of neurostimulation for epilepsy. Epilepsy Curr, 2023, 23(5): 298-302.
27. Lo MC, Widge AS. Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness. Int Rev Psych, 2017, 29(2): 191-204.
28. Ortiz-guerrero G, Gregg NM. Biomarkers for epilepsy deep brain stimulation. J Clin Neurophysiol, 2025, 42(6): 486-492.
29. Trevelyan AJ, Marks VS, Graham RT, et al. On brain stimulation in epilepsy. Brain, 2025, 148(3): 746-752.
30. Gouveia FV, Warsi NM, Suresh H, et al. Neurostimulation treatments for epilepsy: Deep brain stimulation, responsive neurostimulation and vagus nerve stimulation. Neurotherapeutics, 2024, 21(3): e00308.
31. Rao VR, Rolston JD. Unearthing the mechanisms of responsive neurostimulation for epilepsy. Communications Med, 2023, 3(1): 166.
32. Chen Z, Liu KZ. Mechanism and applications of vagus nerve stimulation. Curr Issues Mol Biol, 2025, 47(2): 122.
33. Ding YQ, Guo KL, Wang XJ, et al. Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy. Transl Neurosci, 2023, 14(1): 20220308.
34. Perez-malagon CD, Lopez-gonzalez MA. Epilepsy and deep brain stimulation of anterior thalamic nucleus. Cureus J Med Sci, 2021, 13(9): e18199.
35. Maksimenko VA, Van heukelum S, Makarov VV, et al. Absence seizure control by a brain computer interface. Sci Rep, 2017, 7: 2487.
36. Campos-rodriguez C, Palmer D, Forcelli PA. Optogenetic stimulation of the superior colliculus suppresses genetic absence seizures. Brain, 2023, 146(10): 4320-4335.
37. Shon YM, Park HR, Lee S. Deep brain stimulation therapy for drug-resistant epilepsy: present and future perspectives. J Epilepsy Res, 2025, 15(1): 33-41.
38. Gummadavelli A, Zaveri HP, Spencer DD, et al. Expanding brain-computer interfaces for controlling epilepsy networks: novel thalamic responsive neurostimulation in refractory epilepsy. Front Neurosci, 2018, 12: 474.
39. Neumann WJ, Gilron R, Little S, et al. Adaptive deep brain stimulation: from experimental evidence toward practical implementation. Mov Disord, 2023, 38(6): 937-948.
40. Zou JJ, Meng L, Lin ZR, et al. Ultrasound neuromodulation inhibits seizures in acute epileptic monkeys. Iscience, 2020, 23(5): 101066.
41. Beisteiner R, Hallett M, Lozano AM. Ultrasound neuromodulation as a new brain therapy. Adv Sci, 2023, 10(14): 15689.
42. Nair DR, Laxer KD, Weber PB, et al. Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy. Neurology, 2020, 95(9): E1244-E1256.
43. Li QH, Shan YZ, Wei PH, et al. The comparison of DBS and RNS for adult drug-resistant epilepsy: a systematic review and meta-analysis. Front Hum Neurosci, 2024, 18: 1429223.
44. Salanova V, Sperling MR, Gross RE, et al. The SANTe study at 10 years of follow-up: effectiveness, safety, and sudden unexpected death in epilepsy. Epilepsia, 2021, 62(6): 1306-1317.
45. Feldmann LK, Neumann WJ, Faust K, et al. Risk of infection after deep brain stimulation surgery with externalization and local-field potential recordings: twelve-year experience from a single institution. Stereotact Funct Neurosurg, 2021, 99(6): 512-520.
46. Jung IH, Chang KW, Park SH, et al. Complications after deep brain stimulation: a 21-year experience in 426 patients. Front Aging Neurosci, 2022, 14: 819730.
47. Becker LL, Gratopp A, Prager C, et al. Treatment of pediatric convulsive status epilepticus. Front Neurol, 2023, 14: 1175370.
48. Costa G, Teixeira C, Pinto MF. Comparison between epileptic seizure prediction and forecasting based on machine learning. Sci Rep, 2024, 14(1): 5653.
49. Van mierlo P, Vorderwülbecke BJ, Staljanssens W, et al. Ictal EEG source localization in focal epilepsy: review and future perspectives. Clin Neurophysiol, 2020, 131(11): 2600-2616.
50. Vaziri AY, Makkiabadi B. Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization. Front Neurosci, 2025, 18: 1505017.
51. Jung Y, Mithani K, Suresh H, et al. Deep brain stimulation in pediatric populations: a scoping review of the clinical trial landscape. Stereotact Funct Neurosurg, 2025, 103(2): 132-144.
52. Bergeron D, Iorio-morin C, Bonizzato M, et al. Use of invasive brain-computer interfaces in pediatric neurosurgery: technical and ethical considerations. J Child Neurol, 2023, 38(3-4): 223-238.
53. Bóné B, Kovács N, Balás I, et al. Pregnancy and deep brain stimulation therapy for epilepsy. Epileptic Disord, 2021, 23(4): 633-638.
54. Ding J, Wang L, Wang C, et al. Is vagal-nerve stimulation safe during pregnancy? A mini review. Epilepsy Res, 2021, 174: 106671.
55. Li Y, Eliashiv D, Lahue SC, et al. Pregnancy outcomes of refractory epilepsy patients treated with Brain-responsive neurostimulation. Epilepsy Res, 2021, 169: 106532.
56. Meng LB, Jiang X, Huang J, et al. User identity protection in EEG-based brain–computer interfaces. Ieee Trans Neural Syst Rehabil Eng, 2023, 31: 3576-3586.
57. Pernet CR, Appelhoff S, Gorgolewski KJ, et al. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci Data, 2019, 6: 103.

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Progress and translational challenges of brain-computer interfaces in the treatment of drug resistant epilepsy

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