eess.SPSep 26, 2026
SaveEEG-Based Motor Imagery BCI Algorithms and Technologies: A Review
Organizations: Department of Electrical Engineering, Sharif University of Technology, Azadi St., Tehran, 14588-89694, Iran. · School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.
Abstract
Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the brain and external devices. Among various BCI paradigms, EEG-based motor imagery (MI) has gained prominence due to its simplicity, non-invasiveness, and potential to restore motor function and facilitate rehabilitation for patients with motor impairments. This paper presents a comprehensive review of the most practical processing algorithms developed over the past decade for decoding brain sensorimotor cortex signals. Specifically, this paper discusses the integration of artificial intelligence (AI)-based algorithms, particularly machine learning and deep learning techniques, and their contributions to improving the performance and efficiency of MI-BCI systems in detail. Furthermore, the paper reviews state-of-the-art hardware platforms and emerging converging technologies, including system-on-chip (SoC) architectures, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), wearable devices, the Internet of Things (IoT), and augmented/virtual reality (AR/VR), and discusses their integration with advanced signal processing algorithms to enable next-generation MI-BCI systems. By highlighting current achievements of EEG-based MI-BCI technology and predicting future research directions that could further enhance real-time capabilities, this paper aims to provide valuable insights for researchers and practitioners, fostering innovation in high-performance EEG-based MI-BCI systems.
Figures & tables
Fig. 1: Various medical and non-medical applications of EEG-based BCI modality.
| Search Strings / Search Engine | IEEE Xplore | PubMed | ScienceOPEN | Semantic Scholar | Scopus | Web of Science | Google Scholar |
| (EEG OR electroencephalographic OR electroencephalography) AND (BCI OR ”brain-computer interface”) | 11,751 | 6,770 | 3,610 | 11,900 | 4,916 | 8,665 | 22,600 |
| (EEG OR electroencephalographic OR electroencephalography) AND (BCI OR ”brain-computer interface”) AND (”motor imagery” OR ”motor-imagery” OR MI) | 3,267 | 2,062 | 1,235 | 7,190 | 2,301 | 3,862 | 17,900 |
| (EEG OR electroencephalographic OR electroencephalography) AND (BCI OR ”brain-computer interface”) AND (”machine learning” OR ML OR ”deep learning” OR DL OR ”neural network” OR ”artificial intelligence” OR AI) | 5,230 | 2,961 | 1,547 | 4,040 | 1,101 | 2,449 | 18,100 |
| (EEG OR electroencephalographic OR electroencephalography) AND (BCI OR ”brain-computer interface”) AND (wearable OR wireless) | 806 | 349 | 287 | 3,710 | 1,318 | 776 | 17,700 |
| (EEG OR electroencephalographic OR electroencephalography) AND (BCI OR ”brain-computer interface”) AND (”system-on-chip” OR ”system on a chip” OR SoC OR ”hardware accelerator” OR ”hardware acceleration”) | 398 | 696 | 234 | 3,400 | 44 | 106 | 6,190 |
| (EEG OR electroencephalographic OR electroencephalography) AND (BCI OR ”brain-computer interface”) AND (”Internet of Things” OR IoT) | 284 | 29 | 28 | 301 | 376 | 99 | 11,200 |
TABLE I: Search strings and numbers of records retrieved from the selected databases for EEG-based BCI and related technologies. Searches were conducted in July 2026 and restricted to publications from January 1, 2014, to July 1, 2026.
| Full Term | Abbreviation | Full Term | Abbreviation | Full Term | Abbreviation | Full Term | Abbreviation |
|---|---|---|---|---|---|---|---|
| Motor imagery-based BCI | MI-BCI | Brain-computer interface | BCI | Artificial intelligence | AI | Electroencephalography | EEG |
| Machine learning | ML | Artificial neural network | ANN | Deep learning | DL | Generative AI | GenAI |
| Classification accuracy | CA | Convolutional neural network | CNN | Feedforward neural network | FFNN | Back propagation neural network | BPNN |
| Probabilistic neural network | PNN | Euclidean Space | EUS | System-on-Chip | SoC | Band-pass filtering | BPF |
| Hybrid | HYB | Deep belief network | DBN | Transformer | TRF | Common spatial pattern | CSP |
| Support vector machine | SVM | Linear discriminant analysis | LDA | Neuro‑fuzzy system | NFS | Hardware accelerator | HA |
TABLE II: List of frequently used abbreviations in this paper.
Fig. 2: Overview of brain activity signal acquisition modalities for BCI systems. The modalities are grouped into magnetic (MEG), electric (ECoG, LFP, and EEG), and metabolic (NIRS and fMRI) approaches, highlighting their invasive and non-invasive implementations and representative sensing hardware.
Fig. 3: Brain region labeling and the international standard EEG electrode placement systems. (a) Lateral brain view divided into the six main cortical regions referenced by electrode nomenclature: frontopolar (Fp), frontal (F), central (C), parietal (P), occipital (O), and temporal (T), with the cerebellum and spinal cord shown for anatomical context. (b) 10-20 electrode placement system (21 electrodes), with electrode labels color-coded by region and referenced to the nasion and inion. A1/A2 denote auricular (ear) reference electrodes, which are not cortical sites. (c) 10-10 electrode placement system (extended array of up to 81 electrodes), providing finer spatial resolution between the standard 10-20 sites.
Fig. 4: The MI process in the brain. The first (internal) (a) somatosensory or third (external) (b) observational perspective imagination of opening/closing the fists activates the primary sensorimotor cortex. During imagined movement (c), the specific cortical oscillation rhythms ( , 8–12 Hz and , 13–30 Hz) are suppressed, known as Event-Related Desynchronization (ERD). Upon movement termination, the termination of MI elicits a distinct -band amplitude increase, known as Event-Related Synchronization (ERS) or -rebound.
Fig. 5: The EEG-based MI-BCI life cycle overview. (a) Brain electrical, magnetic, and metabolic signals are generated by neural activity. (b) EEG-based electrical signal acquisition method. (c) Five frequency bands of EEG signals, known as EEG rhythms. (d) MI processing pipeline: analog front end (AFE), digital signal processing (DSP), and actuator interface. (e) Conventional MI experimental paradigms. (f) Actuator to brain feedback mechanisms: Vibration, Tactile, Thermal, Electrical, Haptic, Auditory, and Visual.
| Cat. | Dataset | Channels | Sampling Rate | Subjects | Classes | Task |
|---|---|---|---|---|---|---|
| 1 | BCI Competition III dataset IIIa | 25 | 250 Hz | 9 | 4 | Both hands, one foot, and tongue MI |
| 2 | BCI Competition III dataset IVa | 118 | 100 Hz | 5 | 3 | Both hands and right foot MI |
| 3 | BCI Competition III dataset V | 32 | 512 Hz | 3 | 3 | Both hands and word generation MI |
| 4 | BCI Competition IV dataset 2a | 22 + 3 EOG | 250 Hz | 9 | 4 | Both hands, both feet, and tongue MI |
| 5 | BCI Competition IV dataset 2b | 3 EEG + 3 EOG | 250 Hz | 9 | 2 | Both hands MI |
| 6 | Physionet EEG Motor Movement/Imagery | 64 | 160/128 Hz | 109 | 4 | Both fists and both feet opening/closing MI |
TABLE III: The most common types of benchmark datasets used for evaluating the performance of MI algorithms.
| Ref. | Year | Pre-processing | Feature Extraction | Feature Classification | Dataset Category | Performance |
| [ 138 ] | 2026 | WPD, ICA, SWS | WVG | ConvNeXt | (4), (9) | Dataset (4): CA(90.20%), Spe(95%), Pre(87.8%), K(0.87). Dataset (9): CA(97.73%), Spe(98.3%), Pre(98.57%), K(0.94). |
| [ 143 ] | 2026 | BPF | MAML | MAML-LNN | (4),(6) | Dataset (4): 4-Class CA(81.3%), CTCA(76.3%), 4-Class K(0.75), CT K(0.69), 4-Class macro F1 (0.8), 4-Class macro F1 (0.8), CT macro F1(0.76). Dataset (6): 4-Class CA(79.6%), 4-Class K(0.729), 4-Class macro F1 (0.786). Eff: 0.82 M param., 0.17 G MACs, Speed: 38 fps. |
| [ 144 ] | 2026 | ICA, FIR BPF | TMEF-Net | TMEF-Net | (8) | INACA: 89%, INECA: 81% |
| [ 145 ] | 2025 | ZM-STD | MSCFormer | MSCFormer, FCL | (4),(5) | Dataset (4): CA(82.95%), K(0.7726). Dataset (5): CA(88%), K(0.7599). |
| [ 146 ] | 2025 | VAE | DAE | CNN-LSTM | (8) | CA: 94.36%, Pre: 0.94, Recall: 0.947, F1-Score: 0.94 |
| [ 147 ] | 2025 | BPF, CSP, ICA | PSD (WCM), MRCP, ERD | SVM, LDA, KNN, RF | (8) | CA: SVM (80%), RF (79%), KNN (78%), LDA (60%) (k-fold cross-validation) |
TABLE IV: State-of-the-art EEG-based MI-BCI signal processing algorithms reported in the literature in the recent decade.
Fig. 6: The relation between various categories and sub-domains of artificial intelligence (AI).
Fig. 7: Comparison of traditional processing of EEG-based MI signals and DL-based architectures.
Fig. 8: Taxonomy of EEG pre-processing algorithms for MI-based BCI systems. EEG pre-processing techniques are broadly classified into blind source separation, transform-based, filtering, and regression-based artifact removal approaches. Typical algorithms and their corresponding signal processing mechanisms are illustrated, demonstrating how these methods enhance signal quality, reduce artifacts and noise, and facilitate reliable feature extraction and classification in MI-BCI applications.
Fig. 9: EEG signals Feature Extraction algorithms classifications based on the mathematical domain of the signal representation. The classification spans five foundational domains and a combined approach: Time-Domain Techniques, Frequency-Domain Techniques, Time-Frequency Domain Techniques, Spatial Domain Techniques, Feature Selection/Reduction Techniques, and HYB Techniques.
Fig. 10: Applying FFT conversion to EEG MI signals for extracting the delicate frequency features of MI oscillations. The FFT decomposes the raw, time-domain EEG rhythms into a frequency-domain spectrum, isolating power changes in target bands (e.g., alpha and beta rhythms) to construct a discriminative feature vector for classification.
Fig. 11: EEG wavelet transform-based feature extraction methods. Applying DWT (a) and CWT (b) to MI signals.
Fig. 12: Utilizing CSP-based methods for EEG MI signal feature extraction.
Fig. 13: Summary of prevalent AI-based MI feature classification algorithms.
Fig. 14: Schematic workflow of an EEG-based MI-BCI classification system using the SVM algorithm. The Data Acquisition & Feature Extraction shows the raw EEG signals are recorded from motor cortex channels (e.g., C3, C4) during imagined right-hand vs. left-foot movements, and transformed into a 2D feature space using the PSD. The SVM Classification shows that a linear kernel SVM, for example, identifies the optimal separating hyperplane ( ) by maximizing the margin between the two MI classes, uniquely defined by the critical edge data points known as support vectors.
Fig. 15: Utilizing LDA-based methods for MI feature classification.
Fig. 16: Utilizing KNN-based methods for MI feature classification.
Fig. 17: Utilizing RF-based methods for MI feature classification.
Fig. 18: (a) Natural biological neurons structures. (b) Symbolic mathematical representation of conventional artificial neurons.
Fig. 19: FFNN-type ANNs used as MI feature classifiers.
Fig. 20: The RNN-type ANNs as the MI features classifier.
Fig. 21: Utilizing the CNN-based model for MI features classification.
Fig. 22: The TR-based models as the MI features classifier.
Fig. 23: The GenAI-based models as the MI features classifier.
| Ref. | Year | HW. | Pre-processing | Feature Extraction | Feature Classification | Dataset Category | Performance |
| [ 30 ] | 2026 | Wearable EEG headband, 5 electrodes (4 wet, 1 dry) | NFL, BPF, BWF, CAR | CTCNet | CTCNet, SST | (8) | SICA: 86% |
| [ 197 ] | 2026 | wireless EEG system (InMex; WellFulfill Ltd., Taoyuan, Taiwan); Bluetooth; 8 dry electrodes; Oculus VR headset (paired with EEG cap for immersive AO+MI training) | BPF, BSF | CNN–TRF | CNN–TRF, CNL | (8) | CA: 47.4% (Session 1), 66.2% (Session 3); 52.8% (offline cross-validation benchmark) |
| [ 199 ] | 2025 | recoveriXPRO 16 channels stroke rehabilitation system; Estim FES device; 32-channel g.Nautilus EEG system; TMS device for motor evoked potentials (MEPs)/resting motor threshold (RMT) | BPF, BWF | LAP, PDC | LDA | (8) | Improved muscle strength in the wrist extensor (MRC-WE), MEAD:0.52, p:0.036 |
| [ 14 ] | 2022 | 60-channel EEG signal; Xilinx KC705 FPGA evaluation board; Artix-7 FPGA, Xilinx VC707 FPGA | BPF | EEGNet | EEGNet | (8) | CA: 93.06%; REU reduced to 2.54% area and 3.66% PC |
| [ 258 ] | 2022 | FPGA, 65 nm CMOS LP process | BPF | MC-EEGNet | MC-EEGNet | - | ARE: 87.22%, Eff: 20.77% better than a RISC-V MCU, SPU: 3.7 |
| [ 200 ] | 2021 | Wireless soft scalp electronics system with Samsung Gear VR; 6 flexible microneedle electrodes | BPF, BWF | PSD, WCM, CNN | Spatial CNN, Standard CNN, SVM (cubic polynomial kernel) | (8) | CA: 93.22 1.33% (4-class, spatial CNN) |
TABLE V: State-of-the-art EEG-based MI-BCI hardware implementations reported in the literature in recent decade.
Fig. 24: The EEGNet CNN-based processing architucture for MI signals classification.
Fig. 25: State-of-the-art EEG-based MI-BCI system. The most common and most recent algorithms reported in the literature in recent decade for each blocks of the signal processing pipeline are illustrated.
Fig. 26: Convergence of software and hardware technologies driving the commercialization of next-generation EEG-based MI-BCI applications.
Fig. 27: Different types of EEG electrodes. (a) Dry electrodes. (b) Wet electrodes. (c) Semi-dry electrodes.
Fig. 28: Intelligent integrated MI-BCI ecosystem for personalized neuro interaction.
Fig. 29: Conceptual block diagram and major subsystems of a next-generation wearable edge-processing EEG-based MI-BCI SoC.
Fig. 31: Annual number of MI-BCI publications from January 2014 to July 2026, retrieved from Scopus using the search strategy described in Section II. The connecting line represents the yearly publication counts and does not indicate statistical regression, smoothing, or extrapolation.
Fig. 32: Prevalence percentages of MI signal-processing algorithms and datasets based on the subsets of studies providing sufficient information for each analysis: (a) signal preprocessing algorithms ( studies); (b) feature-extraction algorithm categories ( studies); (c) feature-extraction algorithms ( studies); (d) feature-classification algorithms ( studies); and (e) common MI datasets ( studies).
Fig. 33: Current MI-BCI systems challenges and solutions.
Fig. 34: Trends of converging technologies for EEG-based BCI from January 2014 to July 2026 based on annual publication counts retrieved from Scopus using the search strategy described in Section II: (a) EEG-based BCI and AI. (b) EEG-based BCI and wearable technology. (c) EEG-based BCI and HA technology. (d) EEG-based BCI and IoT. (e) EEG-based BCI and EP. (f) EEG-based BCI and VR. Scopus is used as the reference (see Section II for more details). The connecting lines represent yearly publication counts and do not indicate statistical regression, smoothing, or extrapolation.