EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review
Abstract
:1. Introduction
RQ: Are wearable technologies mature for EEG-based MI-BCI applications in uncontrolled environments?
- RQ1: Is there a significant amount of EEG-based MI-BCI studies using wearable technologies in the literature that implies a promising future development of this research field, especially in uncontrolled environments and outside the medical and clinical settings?
- RQ2: Are there common pipelines of processing that can be adopted from signal acquisition to feedback generation?
- RQ3: Are there consolidated experimental paradigms for wearable EEG-based MI-BCI applications?
- RQ4: Are there datasets available for the research community to properly compare classification models and data analysis?
2. Systematic Review Search Method
2.1. Eligibility Criteria
- Studies published in the last 10 years (from 1 January 2012 to 22 June 2022);
- Studies published as journal articles, conference proceedings, and dataset reports.
- Non-English articles;
- Studies published as meeting abstracts, book chapters, posters, reviews, and Master’s and PhD dissertations.
2.2. Information Sources
2.3. Search Strategy
- A mean (std) of 4.76% (4.98%) works of the EEG and BCI search are present in the EEG and BCI and wearable field;
- A mean (std) of 26.01% (9.38%) works of the EEG and BCI search are present in the EEG and BCI and MI field;
- A mean (std) of 0.71% (0.65%) works of the EEG and BCI search are present in the EEG and BCI and wearable and MI field, the target of the present survey.
2.4. Search Outcome
3. Background Information
3.1. Electroencephalogram
- being non-stationary signals varying across time [18];
- being subject-specific, due to the natural physiological differences between subjects [16];
- varying in the same subject depending on their physiological and psychological conditions, and changing from trial to trial [19];
3.1.1. EEG Rhythms
3.2. Motor Imagery
3.3. Brain–Computer Interfaces
3.4. Wearable Technologies
the evolution of ambulatory EEG units from the bulky, limited lifetime devices available today to small devices present only on the head that can record EEG for days, weeks, or months at a time.
4. Overview of Survey Articles on EEG-Based BCIs
5. EEG-Based MI-BCIs through Wearable Systems
5.1. BCI Application and Feedback
5.2. Employed Technologies
5.3. Signal Processing and Analysis
5.3.1. EEG Data Preprocessing
- Nine works assumed that source signals are statistically independent of each other and instantaneously mixed, and apply Independent Component Analysis (ICA) to remove noise, mainly due to eye movements and eye blinks [93,95,103,141,147,150,152,158,171]. EEGLAB toolbox [195] is frequently employed by the authors to implement ICA;
- A number of 11 works applied temporal filtering approaches such as Butterworth of different orders and cutoffs: third order filter in 0.5–30 Hz [148] or in 4–33 Hz [123], fourth order in 16–24 Hz [88], fifth order in 8–30 Hz [96,114,132,142,143] or in 1–400 Hz [113], biquad tweaked Butterworth in 8–13 Hz [138], and sixth order in 8–30 Hz [153];
5.3.2. Feature Engineering
5.3.3. Classification and Data Analysis
- Questionnaire analyses have also been performed for quality assessment, by considering the opinions given by the subjects concerning a specific device [97], or employing the Quebec User Evaluation of Satisfaction with Assistive Technology test to evaluate patients’ satisfaction [88] and for subject MI ability assessment [113];
5.4. Dataset and Experimental Paradigms
- A total of 7/79 papers do not provide any information regarding the involved subjects;
- A total of 36/79 papers specify the biological gender of the subjects and report most of the time the number of subjects divided per male and female;
- A total of 50/79 papers recruited healthy subjects, while only 5 considered patients affected by specific pathologies;
- A total of 21/79 papers present information regarding the previous experience of the subjects with EEG, BCI, or MI-based experiments;
- Almost 50% of the works reporting information on the subjects perform their experiment on a maximum of 5 participants; 27% recruit a maximum of 10 subjects, and very few works consider more than 20 participants. A detailed infographic is depicted in Figure 8.
5.4.1. BCI Competition III Dataset IIIa
5.4.2. BCI Competition III Dataset IVa
5.4.3. BCI Competition IV Dataset 2a
5.4.4. BCI Competition IV Dataset 2b
5.4.5. EEG Motor Movement/Imagery Dataset
5.4.6. MI-OpenBCI
5.4.7. EEG BCI Dataset
6. Discussion
Are wearable technologies mature for EEG-based MI-BCI applications in uncontrolled environments?
by analyzing the results obtained through the extensive search initially performed considering different EEG, MI, and BCI related keyword combinations (Section 2.3) detailed in Table 2 and the final paper pool identified through the PRISMA flow (Figure 1).RQ1: Is there a significant amount of EEG-based MI-BCI studies using wearable technologies in the literature that implies a promising future development of this research field, especially in uncontrolled environments and outside the medical and clinical settings?
RQ2: Are there common pipelines of processing that can be adopted from signal acquisition to feedback generation?
notice that the experimental paradigm adopted by most of the considered works (39 out of 84) concerns MI of left/right hand/fist movement. However a high number of different types of other MI paradigms are considered: single hand/both hands, foot/feet or tongue movement, shoulder flexion, extension and abduction, the motion of upper/lower limbs, pedaling, game character/robot/machinery movement control or generic motor intention and even the imagination of cognitive tasks. Moreover, single task duration, task order, administration modality, and experimental settings are also very heterogeneous.RQ3: Are there consolidated experimental paradigms for wearable EEG-based MI-BCI applications?
RQ4: Are there datasets available for the research community to properly compare classification models and data analysis?
7. Conclusions and Future Perspectives
The ultimate goal toward smart wearable sensing with edge computing capabilities relies on a bespoke platform embedding sensors, front-end circuit interface, neuromorphic processor and memristive devices.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
1DMSCNN | One-Dimensional Multi-Scale Convolutional Neural Network |
AI | Artificial intelligence |
ANN | Artificial neural network |
AUC | Area Under the Curve |
BCI | Brain–computer interface |
CAR | Common Average Reference |
CNN | Convolutional neural network |
CSP | Common Spatial Pattern |
DBN | Deep Belief Network |
DL | Deep learning |
ECG | Electrocardiogram |
ECoG | Electrocorticography |
EEG | Electroencephalography |
EMG | Electromyography |
EOG | Electrooculography |
ERD | Event-Related Desynchronization |
ERP | Event-Related Potentials |
ErrP | Error-Related Potential |
ERS | Event-Related Synchronization |
FFT | Fast Fourier Transform |
FGMDRM | Framework with filter geodesic minimum distance to Riemannian mean |
FMRI | Functional Magnetic Resonance Imaging |
ICA | Independent Component Analysis |
KNN | K-Nearest Neighbor |
LDA | Linear Discriminant Analysis |
LPA | Left pre-auricolar point |
LR | Logistic Regression |
LSTM | Long-Short Term Memory |
MI | Motor imagery |
ML | Machine Learning |
MLP | Multi-Layer Perceptron |
MSCNN | Multi-Scale Convolutional Neural Network |
NB | Naive Bayes |
NN | Neural network |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
PSD | Power Spectral Density |
PTFBCSP | Penalized Time–Frequency Band Common Spatial Pattern |
QDA | Quadratic Discriminant Analysis |
RF | Random forest |
RNN | Recurrent neural network |
RPA | Right pre-auricolar point |
RQ | Research question |
SJGDA | Semisupervised Joint mutual information with General Discriminate Analysis |
SNR | Signal to Noise Ratio |
SSDT | Subject specific decision tree |
SSVEP | Steady-state visual evoked potential |
SVM | Support vector machine |
TES | Transcranial electrical stimulation |
VR | Virtual reality |
XGBoost | Extreme Gradient Boosting |
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Search Engine | Author | Last Consultation Date |
---|---|---|
IEEE Xplore | F.G. | 22 June 2022 |
Mendeley | F.G. | 22 June 2022 |
PubMed | S.C. | 22 June 2022 |
ScienceOPEN | A.S. | 21 June 2022 |
Semantic Scholar | A.S. | 21 June 2022 |
Scopus | A.S. | 22 June 2022 |
Web of Science | S.C. | 22 June 2022 |
Google Scholar | M.C. and A.S. | 20 June 2022 |
Search Engine | EEG and BCI | EEG, BCI, and Wearable | EEG, BCI, and MI | EEG, BCI, Wearable, and MI |
---|---|---|---|---|
IEEE Xplore | 3584 | 121 | 546 | 13 |
Mendeley | 10,723 | 286 | 3118 | 60 |
PubMed | 2777 | 57 | 877 | 14 |
ScienceOPEN | 2659 | 22 | 303 | 4 |
Semantic Scholar | 12,200 | 1879 | 3630 | 259 |
Scopus | 10,733 | 670 | 2909 | 74 |
Web of Science | 6564 | 180 | 2491 | 40 |
Rhythm | Frequency Range (Hz) | Occurrence |
---|---|---|
≤4 | infants, deep sleep | |
4–8 | emotional stress, drowsiness | |
8–13 | relaxed awake state | |
8–13 | motor cortex functionalities | |
13–30 | alert state, active thinking/attention, anxiety | |
≥31 | intensive brain activity |
Device | Producer | Electrodes | Price | Papers |
---|---|---|---|---|
B-Alert X-Series [164] | Advanced Brain Monitoring | up to 20/W (gel) | ask producer | [102] |
BrainMaster Discovery 24E * [165] | bio-medical | 24/W, D (compatible) | USD 5800.00 | [114] |
Cyton Biosensing Board * [166] | OpenBCI | 8/W, D (compatible) | USD 999.00 | [98,113,162] |
eego rt [167] | ANT Neuro | 8-64/W, D | ask producer | [168] |
Enobio 20 [169] | Neuroelectrics | 20/W, D | ask producer | [88,142] |
Enobio 8 [170] | Neuroelectrics | 8/W, D | ask producer | [93,97,148,162,171,172] |
EPOC+ [173] | Emotiv | 14/W (saline) | discontinued | [95,110,115,121,126,127,129,132,133,135,136,139,156,174,175] |
EPOC Flex [176] | Emotiv | up to 32/W (gel, saline) | USD 1699.00–2099.00 | [122,177] |
EPOC X [178] | Emotiv | 14/W (saline) | USD 849.00 | [107] |
g.USBamp * with(out) g.MOBIlab [179] | g.tec medical engineering | 16/W, D | starting from EUR 11,900.00 | [87,104,120,125] |
Helmate [180] | abmedica | NA/NA | ask producer | [111,157] |
Insight [181] | Emotiv | 5/S | USD 499.00 | [106] |
MindWave Mobile 2 [182] | Neurosky | 1/NA | USD 109.99 | [108,183] |
Muse headband 2 [184] | InteraXon | 4/NA | EUR 269.99 | [109,112,131,141,144] |
g.Nautilus Multi-Purpose * [185] | g.tec medical engineering | 8–64/W, D (compatible) | changing according to configuration, starting from EUR 4990.00 | [89,90,105,125,130,145,146,151,153,155] |
g.Nautilus PRO [186] | g.tec medical engineering | 8–32/W, D | changing according to configuration, starting from EUR 5500.00 | [89,90,105,125,130,145,146,151,153,155] |
g.Nautilus RESEARCH [187] | g.tec medical engineering | 8–64/W, D | changing according to configuration, starting from EUR 4990.00 | [89,90,105,125,130,145,146,151,153,155] |
NuAmps * [188] | NeuroScan | 32/NA | ask producer | [92] |
Quick-20 Dry EEG Headset [189] | Cognionics | 19/D | ask producer | [100,137] |
Starstim [190] | neuroelectrics | 8–32/tES-EEG | ask producer | [103,140] |
Synamps 2/RT * [191] | Neuroscan | 64/NA | ask producer | [118] |
Reference | Dataset and Experimental Paradigm | Classification and/or Other Analyses | Performance of the Best Method | Online and/or Offline |
---|---|---|---|---|
Tang et al. [110] | Benchmark dataset: BCI Competition IV dataset 2b *. Own dataset: not available. Five subjects performed an experiment consisting of 90 repetitions of each of the MI tasks (left/right hand). | Classification task: binary (left vs. right hand). DBN, DWT-LSTM, 1DCNN, 2DCNN and one-dimensional multi-scale convolutional neural network (1DMSCNN). | Measures: average accuracy with 1DMSCNN. Validation strategy: dataset division in training and test sets according to the 4:1 ratio. BCI Competition IV dataset 2b (offline analysis): 82.61%. Own dataset (online analysis): accuracy for each subject 76.78%, 91.78%, 70.00%. | both |
Guan, Zhao, and Yang [133] | Benchmark dataset: BCI Competition IV dataset 2a *, BCI Competition III dataset IIIa *.
Own dataset: available upon request. Seven subjects performed imagination of shoulder flexion, extension, and abduction. The acquisition were repeated for 20 trials, each lasting for 5 s of activity plus 5–7 s rest. | Classification tasks: one-vs.-one, one-vs.-rest. 1. Subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM). 2. Feature extraction algorithm combining semisupervised joint mutual information with general discriminate analysis (SJGDA) to reduce the dimension of vectors in the Riemannian tangent plane and classification with KNN. | Measures: average accuracy and mean kappa value. Validation strategy: k-fold cross validation. BCI Competition IV dataset 2a: - SSDT-FGMDRM 10-fold cross-validation average accuracy left vs. rest 82.00%, right vs. rest 81.28%, foot vs. rest 81.51%, tongue vs. rest 83.95%. SSDT-FGMDRM mean kappa value 0.589. - SJGDA 10-fold cross-validation mean accuracy left vs. rest 84.3%, right vs. rest 83.54%, foot vs. rest 82.11%, tongue vs. rest 85.23%. SJGDA mean kappa value 0.607. - SJGDA 10-fold cross-validation mean accuracy on left vs. right 79.41%, left vs. feet 87.14%, left vs. tongue 86.51%, right vs. feet 86.75%, right vs. tongue 87.00%, feet vs. tongue 82.04%. BCI Competition III dataset IIIa: 5-fold cross-validation mean accuracy on 82.78%. Own dataset: 5-fold cross-validation mean accuracy (rounded values taken from the provided bar graphics) flexion vs. rest 90.00%, extension vs. rest 80.00%, abduction vs. rest a bit more than 90.00% and flexion vs. extension 90.00%, flexion vs. abduction 95.00%, extension vs. abduction 90.00%. | offline |
Peterson et al. [168] | Benchmark dataset: BCI competition III dataset IVa * and BCI competition IV dataset 2b.
Own dataset: 11 subjects performed imagination of dominant hand grasping and a resting condition in four runs constituted by 20 trials per class. | Classification task: binary (rest vs. dominant hand grasping). Optimal feature selection and classification contemporaneously performed through generalized sparse LDA. | Measures: average accuracy (reported best). Validation strategy: 10 × 10 fold cross validation. BCI competition III: 90.94 (±1.06)%. BCI competition IV: 81.23 (±2.46)%. Own dataset: 82.26 (±2.98)%. | offline |
Yang, Nguyen, and Chung [147] | Benchmark dataset: None
Own dataset: available upon request. Six subjects, 10 trials of right hand grasping imagination for 5 s. The experiment was repeated for 10 runs. Notice that SSVEP tasks were also included. | Classification task: multi-class both MI and SSVEP. CNN. | Measures: best average accuracy (MI task).
Own dataset: 91.73 (±1.55)%. | offline |
Freer, Deligianni, and Yang [130] | Benchmark dataset: BCI competition IV dataset 2a.
Own dataset: three subjects performed a paradigm without and with feedback. A total of 20 trials for each of the four conditions (left/right hand, both hands/feet) are performed per run with MI of 2–3 s. | Classification task: multi-class (4 classes). Adaptive Riemannian classifier. | Measures: accuracy.
BCI Competition IV dataset 2a: lower than 50%. Own dataset: lower than 50%. | both |
Barria et al. [88] | Benchmark dataset: None
Own dataset: available (https://www.clinicaltrials.gov/ct2/show/NCT04995367 accessed on 31 January 2023). Five subjects, five phases: calibration, real movement, stationary therapy, MI detection with visual stimulation, and MI detection with visual and haptic stimulation. Besides the first phases, the protocol consisted of 10 s alternations of knee flection task and rest. | Classification task: None. Other analyses: - Control of ankle exoskeleton through knee flection. - Analysis of the success rate in using the BCI, based on the beta power rebound threshold. - Quebec User Evaluation of Satisfaction with Assistive Technology test to evaluate patients’ satisfaction. | None. | offline |
Peterson et al. [113] | Benchmark dataset: None Own dataset *: https://github.com/vpeterson/MI-OpenBCI (accessed on 31 January 2023). | Classification task: binary (rest vs. dominant hand grasping). Generalized sparse discriminant analysis is used for both feature selection and classification. Other analyses: - The motor imagery ability of a single subject has been accessed through the KVIQ-10 questionnaire. - Analyses of temporal and frequency information. | Measures: average accuracy (extracted from bar plot). Own dataset: around 85% with Penalized Time–Frequency Band Common Spatial Pattern (PTFBCSP). | both |
Shajil, Sasikala, and Arunnagiri [143] | Benchmark dataset: BCI competition IV dataset 2a. Own dataset: nine subjects performed 80 trials per MI conditions: left and right hand. | Classification task: binary. AlexNet, ResNet50, and InceptionV3 (pre-trained CNN models) plus transfer learning. | Measures: best average accuracy. BCI competition IV dataset 2a: InceptionV3 82.78 (±4.87)%. Own dataset: InceptionV3 83.79 (±3.49)%. | offline |
Zhang et al. [115] | Benchmark dataset: used 10 subjects of Physionet EEG Motor Movement/Imagery Dataset *. Own dataset: seven subjects. Five conditions: eyes closed, left/right hand, both hands/feet paradigm (as for the benchmark dataset). | Classification task: multi-class on five conditions. RNN, CNN, RNN + CNN. | Measures: average accuracy. (Precision, Recall, F1, AUC and confusion matrix for both Physionet and own dataset are also provided). Validation: - Benchmark dataset divided into training (21,000 samples) and test sets (7000 samples). - Own dataset divided into training (25,920 samples) and test sets (8640 samples) for each subject. Benchmark dataset: best model RNN+CNN 95.53% average accuracy. Own dataset: best model RNN+CNN 94.27% average accuracy. | offline |
Mwata et al. [126] | Benchmark dataset: EEG BCI dataset *. Own dataset: four subjects. Experimental conditions: right and left hand, and the neutral action. | Classification task: multi-class on three conditions (neutral, left/right with corresponding robot command forward, backward, and neutral). Hybrid CNN-LSTM model. Other analyses: Report different subject-combinations based-analysis. | Measures: average accuracy. Validation strategy: 10-fold cross validation. Benchmark dataset: 79.2%. Own dataset: 84.69%. | online |
Apicella et al. [111] | Benchmark dataset: None. Own dataset: 17 subjects. Motor task consists of maintaining attention focused only on (i) the squeeze movement (attentive-subject trial), or (ii) a concurrent distractor task (distracted-subject trial); in both trials, the participant must perform the squeeze-ball movement (three sessions, 30 trials per session). Total epochs: 4590. Half of the epochs were collected during the attentive-subject trials and were labeled as belonging to the first class. The remaining part was acquired during the distracted-subject trials and was labeled as belonging to the second class. | Classification task: binary (MI during attention vs. MI during distraction). KNN, SVM, ANN, LDA, NB. | Measures: average accuracy (also provide precision, recall and F1 measure). Validation strategy: 10-fold cross validation. Own dataset: k-NN 92.8 (±1.6)%. | offline |
Alanis and Gutiérrez [119] | Benchmark dataset: None. Own dataset: available upon request, two subjects, four conditions: left or right hand, both hands, move up and down both feet. Five runs of forty trials. | Classification tasks: binary one-vs.-rest. LDA classifier using features extracted by BCI2000.
Other analyses: graph theory metrics to understand the differences in functional brain connectivity. | Best binary classification for both subjects: right hand open/close vs. rest. No classification results reported. | online |
Mahmood et al. [123] | Benchmark dataset: None. Own dataset: available upon request, four subjects. Experimental conditions: eyes closed, left/right hand, pedal pressing. | Classification tasks: multiclass (4 classes). Population-based approach. SVM and CNN classifiers. | Measures: average accuracy. Validation strategy: 5-fold cross validation. CNN real-time accuracy: 89.65% and 93.22% for Ag/AgCl and FMNEs electrodes | both |
Dataset | Link | Device | Experimental Paradigm |
---|---|---|---|
BCI Competition III dataset IIIa [196] | https://www.bbci.de/competition/iii/ | Neuroscan, 64 channel EEG amplifier (wired) | cue-based left/right hand, foot, tongue MI |
BCI Competition III dataset IVa [196] | https://www.bbci.de/competition/iii/ | BrainAmps and 128 channel ECI cap (wired) | cue-based left/right hand, right foot MI |
BCI Competition IV dataset 2a [197] | https://www.bbci.de/competition/iv/ | 22 electrodes (wired) | cue-based MI-BCI left/right hand, both feet, tongue MI |
BCI Competition IV dataset 2b [197] | https://www.bbci.de/competition/iv/ | 3 electrodes (wired) | cue-based MI-BCI left/right hand MI |
EEG Motor Movement/Imagery Dataset [42,198] | https://physionet.org/content/eegmmidb/1.0.0/ | 64 electrodes (wired) | cue-based motor execution/imagination left/right fist and both feet/fists opening/closing |
MI-OpenBCI [113] | https://github.com/vpeterson/MI-OpenBCI | OpenBCI Cyton and Daisy Module, Electrocap System II, 15 electrodes (wearable) | cue-based dominant hand grasping MI |
EEG BCI dataset [199] | https://figshare.com/collections/A_large_electroencephalographic_motor_imagery_dataset_for_electroencephalographic_brain_computer_interfaces/3917698 | EEG-1200 JE-921A EEG system, 19 electrodes (wired) | left/right hand, left/right leg, tongue, and finger MI |
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Saibene, A.; Caglioni, M.; Corchs, S.; Gasparini, F. EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. Sensors 2023, 23, 2798. https://doi.org/10.3390/s23052798
Saibene A, Caglioni M, Corchs S, Gasparini F. EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. Sensors. 2023; 23(5):2798. https://doi.org/10.3390/s23052798
Chicago/Turabian StyleSaibene, Aurora, Mirko Caglioni, Silvia Corchs, and Francesca Gasparini. 2023. "EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review" Sensors 23, no. 5: 2798. https://doi.org/10.3390/s23052798