Brain-computer Interaction in the Smart Era

Abstract

The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object’s mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients’ physical and psychological status, thereby contributing to improvements in their health and quality of life.

References

[1] Zheng MX, Hua XY, Feng JT, et al. . Trial of Contralateral Seventh Cervical Nerve Transfer for Spastic Arm Paralysis. N Engl J Med, 2018, 378(1): 22-34.

[2] Van Den Brand R, Heutschi J, Barraud Q, et al. . Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science, 2012, 336(6085): 1182-1185.

[3] Rosenfeld JV, Wong YT. Neurobionics and the brain-computer interface: current applications and future horizons. Med J Aust, 2017, 206(8): 363-368.

[4] Pillette L, Lotte F, N’kaoua B, et al. . Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies. Neuroimage Clin, 2020, 28: 102417.

[5] Carelli L, Solca F, Faini A, et al. . Brain-Computer Interface for Clinical Purposes: Cognitive Assessment and Rehabilitation. Biomed Res Int, 2017, 2017: 1695290.

[6] Mane R, Wu Z, Wang D. Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: a perspective review. Stroke Vasc Neurol, 2022, 7(6): 541-549.

[7] Wolpaw JR, Birbaumer N, Mcfarland DJ, et al. . Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.

[8] Hughes C, Herrera A, Gaunt R, et al. . Bidirectional brain-computer interfaces. Handb Clin Neurol, 2020, 168: 163-181.

[9] Wan C, Pei M, Shi K, et al. . Toward a Brain-Neuromorphics Interface. Adv Mater, 2024 e2311288

[10] Ma Y, Gong A, Nan W, et al. . Personalized Brain-Computer Interface and Its Applications. J Pers Med, 2022, 13(1): 46.

[11] De Vico Fallani F, Bassett DS. Network neuroscience for optimizing brain-computer interfaces. Phys Life Rev, 2019, 31: 304-309.

[12] Klein E. Ethics and the emergence of brain-computer interface medicine. Handb Clin Neurol, 2020, 168: 329-339.

[13] Liu Y, Zhao Z, Xu M, et al. . Decoding and synthesizing tonal language speech from brain activity. Sci Adv, 2023, 9(23): eadh0478.

[14] Li Y, Zhang XR, Zhang B, et al. . A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(6): 1170-1180.

[15] Maslen H, Rainey S. Control and Ownership of Neuroprosthetic Speech. Philos Technol, 2021, 34(3): 425-445.

[16] Al-Nafjan A, Al-Wabil A, Almudhi A, et al. . Measuring and monitoring emotional changes in children who stutter. Comput Biol Med, 2018, 102: 138-150.

[17] Zijlmans M, Worrell GA, Dumpelmann M, et al. . How to record high-frequency oscillations in epilepsy: A practical guideline. Epilepsia, 2017, 58(8): 1305-1315.

[18] Baillet S. Magnetoencephalography for brain electrophysiology and imaging. Nat Neurosci, 2017, 20(3): 327-339.

[19] Logothetis NK. What we can do and what we cannot do with fMRI. Nature, 2008, 453(7197): 869-878.

[20] Ferrari M, Quaresima V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage, 2012, 63(2): 921-935.

[21] Chaddad A, Wu Y, Kateb R, et al. . Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. Sensors (Basel), 2023, 23(14): 6434.

[22] Mridha MF, Das SC, Kabir MM, et al. . Brain-Computer Interface: Advancement and Challenges. Sensors (Basel), 2021, 21(17): 5746.

[23] List A, Rosenberg MD, Sherman A, et al. . Pattern classification of EEG signals reveals perceptual and attentional states. PLoS One, 2017, 12(4): e0176349.

[24] Besserve M, Jerbi K, Laurent F, et al. . Classification methods for ongoing EEG and MEG signals. Biol Res, 2007, 40(4): 415-437.

[25] Fleury M, Figueiredo P, Vourvopoulos A, et al. . Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng, 2023, 20(5): 051003.

[26] Pandria N, Athanasiou A, Konstantara L, et al. . Advances in biofeedback and neurofeedback studies on smoking. Neuroimage Clin, 2020, 28: 102397.

[27] Enriquez-Geppert S, Smit D, Pimenta MG, et al. . Neurofeedback as a Treatment Intervention in ADHD: Current Evidence and Practice. Curr Psychiatry Rep, 2019, 21(6): 46.

[28] Dauden Roquet C, Sas C. A Mindfulness-Based Brain-Computer Interface to Augment Mandala Coloring for Depression: Protocol for a Single-Case Experimental Design. JMIR Res Protoc, 2021, 10(1): e20819.

[29] Ramos-Fresnedo A, Perez-Vega C, Domingo RA, et al. . Motor Cortex Stimulation for Pain: A Narrative Review of Indications, Techniques, and Outcomes. Neuromodulation, 2022, 25(2): 211-221.

[30] Schmoigl-Tonis M, Schranz C, Muller-Putz GR. Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review. Front Hum Neurosci, 2023, 17: 1251690.

[31] Hermann JK, Capadona JR. Understanding the Role of Innate Immunity in the Response to Intracortical Microelectrodes. Crit Rev Biomed Eng, 2018, 46(4): 341-367.

[32] Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci, 2001, 24: 167-202.

[33] Barnova K, Mikolasova M, Kahankova RV, et al. . Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med, 2023, 163: 107135.

[34] Ko W, Jeon E, Jeong S, et al. . A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces. Front Hum Neurosci, 2021, 15: 643386.

[35] Huang X, Xu Y, Hua J, et al. . A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface. Front Neurosci, 2021, 15: 733546.

[36] Lu H. Classifying Electroencephalogram Signal Using Underdetermined Blind Source Separation and Common Spatial Pattern. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi (chinese), 2016, 33(2): 216-220

[37] Brumberg JS, Pitt KM, Burnison JD. A Noninvasive Brain-Computer Interface for Real-Time Speech Synthesis: The Importance of Multimodal Feedback. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(4): 874-881.

[38] Ingel A, Kuzovkin I, Vicente R. Direct information transfer rate optimization for SSVEP-based BCI. J Neural Eng, 2019, 16(1): 016016.

[39] Bulhoes Da Silva Costa T, Fernanda Suarez Uribe L, Negreiros De Carvalho S, et al. . Channel capacity in brain-computer interfaces. J Neural Eng, 2020, 17(1): 016060.

[40] Papageorgiou TD, Lisinski JM, Mchenry MA, et al. . Brain-computer interfaces increase whole-brain signal-to-noise. Proc Natl Acad Sci U S A, 2013, 110(33): 13630-13635.

[41] Pfurtscheller G, Allison BZ, Brunner C, et al. . The hybrid BCI. Front Neurosci, 2010, 4: 30

[42] Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev, 2009, 33(2): 81-88.

[43] Kwon OY, Lee MH, Guan C, et al. . Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks. IEEE Trans Neural Netw Learn Syst, 2020, 31(10): 3839-3852.

[44] Castillos KC, Ladouce S, Darmet L, et al. . Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience. Neuroimage, 2023, 284: 120446.

[45] Khalil K, Asgher U, Ayaz Y. Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface. Sci Rep, 2022, 12(1): 3198.

[46] Wei Q, Ding X. Intra- and intersubject common spatial pattern for reducing calibration effort in MI-based BCI. IEEE Trans Neural Syst Rehabil Eng, 2023 PP

[47] Jin J, Li S, Daly I, et al. . The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(1): 3-12.

[48] Jiang X, Bian GB, Tian Z. Removal of Artifacts from EEG Signals: A Review. Sensors (Basel), 2019, 19(5): 987.

[49] Issar D, Williamson RC, Khanna SB, et al. . A neural network for online spike classification that improves decoding accuracy. J Neurophysiol, 2020, 123(4): 1472-1485.

[50] Semenova N, Brunner D. Noise-mitigation strategies in physical feedforward neural networks. Chaos, 2022, 32(6): 061106.

[51] Tian CW, Zheng MH, Zuo WM, et al. . A cross Transformer for image denoising. Inform Fusion, 2024, 102: 102043.

[52] Metsomaa J, Sarvas J, Ilmoniemi RJ. Blind Source Separation of Event-Related EEG/MEG. IEEE Trans Biomed Eng, 2017, 64(9): 2054-2064.

[53] Shao X, Lin M. Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification. Cogn Neurodyn, 2020, 14(5): 689-696.

[54] Dai Y, Duan F, Feng F, et al. . A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition. Entropy (Basel), 2021, 23(9): 1170.

[55] Borirakarawin M, Punsawad Y. Event-Related Potential-Based Brain-Computer Interface Using the Thai Vowels’ and Numerals’ Auditory Stimulus Pattern. Sensors (Basel), 2022, 22(15): 5864.

[56] Siribunyaphat N, Punsawad Y. Steady-State Visual Evoked Potential-Based Brain-Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern. Sensors (Basel), 2022, 22(4): 1439.

[57] Erfanian A, Mahmoudi B. Real-time ocular artifact suppression using recurrent neural network for electroencephalogram based brain-computer interface. Med Biol Eng Comput, 2005, 43(2): 296-305.

[58] Gong P, Wang P, Zhou Y, et al. . A Spiking Neural Network with Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng, 2023 PP

[59] Deny P, Cheon S, Son H, et al. . Hierarchical Transformer for Motor Imagery-Based Brain Computer Interface. IEEE J Biomed Health Inform, 2023, 27(11): 5459-5470.

[60] Abdoli N, Salari N, Darvishi N, et al. . The global prevalence of major depressive disorder (MDD) among elderly individuals: A systematic review and meta-analysis. Neurosci Biobehav Rev, 2022, 132: 1067-1073.

[61] Vermani M, Marcus M, Katzman MA. Rates of detection of mood and anxiety disorders in primary care: a descriptive, cross-sectional study. Prim Care Companion CNS Disord, 2011, 13(2): PCC.10-01013

[62] Toroyan T. Global status report on road safety. Inj Prev, 2009, 15(4): 286.

[63] Borghini G, Astolfi L, Vecchiato G, et al. . Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev, 2014, 44: 58-75.

[64] Kang C, Novak D, Yao X, et al. . Classifying and Scoring Major Depressive Disorders by Residual Neural Networks on Specific Frequencies and Brain Regions. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 2964-2973.

[65] Zeng H, Li XF, Borghini G, et al. . An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction. Sensors (Basel), 2021, 21(7): 2369.

[66] Hosseini MP, Hosseini A, Ahi K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev Biomed Eng, 2021, 14: 204-218.

[67] Echtioui A, Zouch W, Ghorbel M, et al. . Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network. Clin EEG Neurosci, 2023 15500594221148285

[68] Zhi H, Yu Z, Yu T, et al. . A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 3988-3998.

[69] Rezeika A, Benda M, Stawicki P, et al. . Brain-Computer Interface Spellers: A Review. Brain Sci, 2018, 8(4): 57.

[70] Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil Eng, 2000, 8(2): 174-179.

[71] Pratyush P, Pokharel S, Saigo H, et al. . pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pretrained protein language model. BMC Bioinformatics, 2023, 24(1): 41.

[72] Correia J, Formisano E, Valente G, et al. . Brain-Based Translation: fMRI Decoding of Spoken Words in Bilinguals Reveals Language-Independent Semantic Representations in Anterior Temporal Lobe. J Neurosci, 2014, 34(1): 332-338.

[73] Ramsey NF, Salari E, Aarnoutse EJ, et al. . Decoding spoken phonemes from sensorimotor cortex with high-density ECoG grids. Neuroimage, 2018, 180(PtA): 301-311.

[74] Borchert RJ, Azevedo T, Badhwar A, et al. . Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement, 2023, 19(12): 5885-5904.

[75] Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia, 2019, 60(10): 2037-2047.

[76] Filippi M, Preziosa P, Arnold DL, et al. . Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective. J Neurol, 2023, 270(3): 1286-1299.

[77] Wang MY, Jia CG, Xu HQ, et al. . Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients. Curr Med Sci, 2023, 43(2): 336-343.

[78] Liu K, Yin M, Cai Z. Research and application advances in rehabilitation assessment of stroke. J Zhejiang Univ Sci B, 2022, 23(8): 625-641.

[79] Dutta A. Brain-Computer Interface Spellers for Communication: Why We Need to Address Their Security and Authenticity. Brain Sci, 2020, 10(3): 139.