Citation | Features | Labels | ML algorithms | Accuracy rates | DOI |
---|---|---|---|---|---|
Aldayel et al. [2] | DEAP dataset, power spectral density and asymmetry | Preference ratings | DNN, KNN, SVM, RF | 0.94, 0.88, 0.62, 0.92 | |
Aldayel et al. [3] | DEAP dataset, power spectral density | Preference ratings | KNN, SVM, RF, DNN | 0.73, 0.81, 0.87, 0.83 | |
Al-Nafjan [6] | PCA, mRMR, RFE, ReliefF feature selection algorithms | Like/Dislike self-report responses to Commercial products | DNN, SVM, KNN, LDA, RF | 0.93, 0.81, 0.78, 0.70, 0.82 | |
Alimardani and Kaba [4] | Raw EEG signal | Product Choices and movie ratings | CNN, Ensemble Model | 0.51–0.75, 0.51–0.64 | |
Bandara et al. [23] | PCA feature selection from power spectral density | Movie trailer preference | RF, SVC | 91.97, 91.70 | |
Barnett and Cerf [24] | EEG cross-brain correlation | Product willingness to pay | Linear Regression | 0.66 | |
Bhushan et al. [31] | TF synchronisation | Binary preference | ANN | 74.3 | |
Gauba et al. [85] | EEG TF effects | Video advertisement preference | RF, DT, Linear regression | 0.68, 0.33, 0.041 | |
Adrián et al. [1] | HR, GSR and EEG TF effects | Video advertisement Valence score | Logistic Regression, SVM, RF | 0.66, 0.67, 0.89 | |
Golnar-Nik et al. [87] | EEG power | Like/dislike ratings | SVM, LDA | 0.87, 0.90 | |
Guo and Elgendi [101] | TF effects, prepurchase ratings | E-commerce purchase behaviour | Recommender system | N/A | |
Guo et al. [98] | ET, and TF power | Aesthetic preference | SVM, KNN, RF, XGBoost | 0.54, 0.61, 0.59. 0.56 | |
Guixeres et al. [96] | EEG, HRV, ET | Youtube advertisement like/dislike and recall scores | ANN | 0.83 | |
Hakim et al. [102] | Frontal band power, hemispheric asymmetry, inter-subject correlations | Product preference | SVM, Logistic regression, DT, KNN, | 0.69, 0.67, 0.63, 0.60 | |
Hakim et al. [103] | Frontal band power, hemispheric asymmetry, inter-subject correlations | Product Preference | SVM, Logisitic Regression, KNN, TREE | 0.69, 0.67, 0.63, 0.60 | |
Khushaba et al. [139] | Eye-tracking fixation and time–frequency effects | Product preference | Logistic Regression | N/A | |
Kumar et al. [151] | Raw EEG signal | Product valence scores | RF | 0.74 | |
Pandey et al. [206] | Discrete Wavelet Transform power and entropy for 5 frequency bands | Movie trailer Likert ratings (rating, familiarity, purchase intent, willingness to spend) | KNN, RF, MP | 0.72, 0.71, 0.67 | |
Phutela et al. [210] | Wavelet coefficient, power spectral density, and Hjorth parameters | Advertisement and product preference | NB, SVM, KNN, DT, DL | 0.66, 0.66, 0.55, 0.54, 0.55 | |
Ma et al. [178] | ERP | Brand extension acceptance rates | T-SNE algorithm | 0.87 | |
Mashrur et al. [185] | Frequency, time, and time–frequency metrics over frontal electrodes | Purchase intention and affective attitude towards advertisements | SVM | 0.84 | |
Mashrur et al. [186] | Frequency, time, and time–frequency metrics over frontal electrodes | Affective attitude towards E-commerce products | SVM | 0.94 | |
Roberts et al. [223] | ERP and eye-movements | Product willingness to pay | Independent component analysis | N/A | |
Shestyuk et al. [237] | Frontal asymmetry (alpha/beta), fronto-central power (alpha/theta + theta/gamma) | TV viewership and twitter activity | Linear regression, multiple regression | 0.63, 0.72 | |
Soria Morillo et al. [254] | Frequency band power | Advertisement like/dislike ratings | ANN | 0.75 | |
Soria Morillo et al. [255] | Frequency band power | Advertisement like/dislike ratings | ANN | 0.82 | |
Slanzi et al. [251] | Gaze position, pupil dilation, and EEG TF | Website click choice | SVC, logistic regression, neural network | 0.69, 0.71, 0.62 | |
Tyson-Carr et al. [262] | ERP and eye-movements | Product willingness to pay | Independent component analysis | N/A | |
Ullah et al. [263] | Wavelet transformed EEG data | E-commerce product like/dislike ratings | SVM, DT, KNN, ANN | 0.80, 0.68, 0.76, 0.81 | |
Wang et al. [287] | EEG and ET data | Self-reported preference | PCA, Random Lasso, SVM | 0.80, 0.75,0.92 | |
Wei et al., [291] | EEF TF data | Advertisement effectiveness | SVM | 0.75 | |
Yadava et al. [294] | EEG TF effects | Like/dislike ratings of e-commerce products | HMM | 0.68 | |
Yılmaz et al. [303] | Power spectral density | Product like/dislike ratings | Logistic regression | N/A | |
Zamani and Naieni [307] | Band power over five brain lobes | Like/dislike ratings of E-commerce products | SVM, ANN, RF | 0.87, 0.70, 0.81 | |
Zeng et al. [308] | Power spectral density, hemispheric asymmetry, differential entropy, Hjorth parameters | Sport shoes like/dislike ratings | KNN, SVM | 0.94, 0.8 | |
Zheng et al. [313] | PSD | Emotion classification | DNN | 0.85 | |
Zhu et al. [314] | EEG and ET mixed measures | Self-reported preference | SVM, RF, CNN | 70.26, 72.15, 96.4 |