Reference Preprocessing techniques Classifiers Performance Latency (ms) Offline or online system Single-trial analysis Limitations (Yom-Tov and Inbar, 2003) [43 ] Low-pass filter (10 Hz) using 8th-order Chebyshev Simple threshold element, support vector machine (SVM), and linear vector quantiser 3-feature reduction with 1-nearest neighbor (1-NN) Using hybrid detector 25% improvement in performance was achieved as compared to Mason-Birch low frequency asynchronous detector (LFASD) 25 decisions s−1 Offline — Detector fails to work correctly partly due to MRPs related to other limbs and imagined movements (Haw et al., 2006) [60 ] Building a specific template during 3 or 4 training sessions for each subject Thresholding based on correlation and error Accuracy was 70% with a false positive rate (FPR) of (5/24) — — Yes Variability in performance between users (Bai et al., 2007) [61 ] Low pass filter (100 Hz) using 3rd-order Butterworth filter Linear Mahalanobis Distance (MD), Quadratic MD, Bayesian Classifier (BC), Multilayer Perceptron (MLP) Neural Network, Probabilistic Neural Networks, and SVM Accuracy was 75% — Offline Yes Large number of electrodes (122) (Boye et al., 2008) [53 ] Downsampling from 500 Hz to 20 Hz, with antialiasing prefiltering (0–5 Hz) and PCA and Locality Preserving Projection (LPP) A variation of Sensitivity for SVM = 96.3 ± 2.0% for — — Yes Method was tested on segmented data rather than ongoing EEG traces with only 1 subject (Kato et al., 2011) [34 ] Low pass filter (35 Hz) and high pass filter (0.05 Hz) for EEG and 0.1 Hz for EOG SVM Detection rate (intention to switch = 99.3% and (not to switch = 2.1%) — Both Yes Online system cannot differentiate between intend to switch and do not intend to switch (Niazi et al., 2011) [42 ] Band pass filter (0.05–10 Hz) with Optimized Spatial Filter (OSF)
Neyman Pearson Lemma For healthy subject’s movement execution TPR = 82.5 ± 7.81% and for movement imagination TPR = 64.5 ± 5.33% −66.6 ± 121
Offline
Yes Small sample size (patients) and no online detection due to instrumentational limitation For stroke patients TPR = 55.01 ± 12.01% −56.8 ± 139 (Lew et al., 2012) [63 ] Narrow band zero phase noncausal IIR filter with cutoff frequencies of 0.1 and 1 Hz
Linear Discriminant Analysis (LDA) TPR = 76 ± 7% (healthy) −167 ± 68 (healthy)
Offline
Yes
Large number of electrodes (34) For stroke and control subjects TPR = 81 ± 11% (left hand) versus (right hand) TPR = 79 ± 12% Right hand = −140 ± 92 versus left hand = −162 ± 105 (Niazi et al., 2012) [19 ] Band pass filter (0.1–100 Hz) and OSF Matched Filter TPR = 67.15 ± 7.87% and FPR = 22.05 ± 9.07% −125 ± 309 (offline) Online — Different aspects of triggered stimulations were not fully considered (Niazi et al., 2013) [65 ]
Band pass filter (0.05–10 Hz) and OSF to maximize SNR
Matched Filter For motor execution (healthy) TPR = 69 ± 21% and FPR = 2.8 ± 1.7 −196 ± 162
Offline
Yes
— For stroke patients TPR = 58 ± 11% and FPR = 4.1 ± 3.9 152 ± 239 For motor imagery (healthy) TPR = 65 ± 22% and FPR = 4.0 ± 1.7 — (Ahmadian et al., 2013) [64 ] Filtering data between 0.1 Hz and 70 Hz Independent component analysis (ICA) Computation time for constraint blind source extraction (CBSE) algorithm was 0.26 s and blind source separation (BSS) algorithm took 51.90 s 260 — Yes Large number of electrodes (128) with small number of subjects (Jochumsen et al., 2013) [39 ] Band-pass filter (0.05–10 Hz) using 2nd-order Butterworth in forward and reverse direction with three spatial filters, large Laplacian spatial filter (LLSF), OSF, and common spatial patterns (CSP) SVM TPR = ~80% and FPR <1.5 accuracy = 80 ± 10% (speed) and 75 ± 9% (force) 317 ± 73 Offline Yes Inclusion of only healthy subjects (Jiang et al., 2015) [66 ] ICA followed by LSF to enhance SNR ICA TPR = 76.9 ± 8.97% and FPR = 2.93 ± 1.09 per minute −180 ± 354 Offline Yes Prediction of gait initiation was not done (Xu et al., 2014) [20 ] Band-pass filter (0.05–3 Hz) and large LSF to enhance SNR LPP followed by LDA LPP-LDA TPR = 79 ± 12% FPR = 1.4 ± 0.8 per minute 315 ± 165 Online — Inclusion of only healthy subjects and classifier did not work for training trials less than 15
