Table 4.

A systematic table summarising the machine-learning studies included in the present review, including the features, labels, and algorithms used, as well as the accuracy rates achieved

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	https://doi.org/10.3390/app10041525
Aldayel et al. [3]	DEAP dataset, power spectral density	Preference ratings	KNN, SVM, RF, DNN	0.73, 0.81, 0.87, 0.83	https://doi.org/10.3389/fnhum.2020.604639
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	https://doi.org/10.7717/peerj-cs.944
Alimardani and Kaba [4]	Raw EEG signal	Product Choices and movie ratings	CNN, Ensemble Model	0.51–0.75, 0.51–0.64	https://doi.org/10.1145/3460881.3460930
Bandara et al. [23]	PCA feature selection from power spectral density	Movie trailer preference	RF, SVC	91.97, 91.70	https://doi.org/10.1109/ICIIS53135.2021.9660742
Barnett and Cerf [24]	EEG cross-brain correlation	Product willingness to pay	Linear Regression	0.66	https://doi.org/10.1093/jcr/ucw083
Bhushan et al. [31]	TF synchronisation	Binary preference	ANN	74.3	https://doi.org/10.1371/journal.pone.0043351
Gauba et al. [85]	EEG TF effects	Video advertisement preference	RF, DT, Linear regression	0.68, 0.33, 0.041	https://doi.org/10.1016/j.neunet.2017.01.013
Adrián et al. [1]	HR, GSR and EEG TF effects	Video advertisement Valence score	Logistic Regression, SVM, RF	0.66, 0.67, 0.89	https://doi.org/10.3389/fncom.2016.00074
Golnar-Nik et al. [87]	EEG power	Like/dislike ratings	SVM, LDA	0.87, 0.90	https://doi.org/10.1016/j.physbeh.2019.04.025
Guo and Elgendi [101]	TF effects, prepurchase ratings	E-commerce purchase behaviour	Recommender system	N/A	https://doi.org/10.12720/joams.1.1.61–65
Guo et al. [98]	ET, and TF power	Aesthetic preference	SVM, KNN, RF, XGBoost	0.54, 0.61, 0.59. 0.56	https://doi.org/10.1016/j.ergon.2019.02.006
Guixeres et al. [96]	EEG, HRV, ET	Youtube advertisement like/dislike and recall scores	ANN	0.83	https://doi.org/10.3389/fpsyg.2017.01808
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	https://doi.org/10.1101/317073
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	https://doi.org/10.1016/j.ijresmar.2020.10.005
Khushaba et al. [139]	Eye-tracking fixation and time–frequency effects	Product preference	Logistic Regression	N/A	https://doi.org/10.1016/j.eswa.2012.12.095
Kumar et al. [151]	Raw EEG signal	Product valence scores	RF	0.74	https://doi.org/10.1016/j.inffus.2018.11.001
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	https://arxiv.org/abs/2007.10756
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	https://arxiv.org/abs/2206.07484
Ma et al. [178]	ERP	Brand extension acceptance rates	T-SNE algorithm	0.87	https://doi.org/10.3389/fnhum.2021.610890
Mashrur et al. [185]	Frequency, time, and time–frequency metrics over frontal electrodes	Purchase intention and affective attitude towards advertisements	SVM	0.84	https://doi.org/10.3389/fnhum.2022.861270
Mashrur et al. [186]	Frequency, time, and time–frequency metrics over frontal electrodes	Affective attitude towards E-commerce products	SVM	0.94	https://doi.org/10.1016/j.physbeh.2022.113847
Roberts et al. [223]	ERP and eye-movements	Product willingness to pay	Independent component analysis	N/A	https://doi.org/10.3389/fnins.2018.00910
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	https://doi.org/10.1371/journal.pone.0214507
Soria Morillo et al. [254]	Frequency band power	Advertisement like/dislike ratings	ANN	0.75	https://doi.org/10.1186/s12938-016–0181-2
Soria Morillo et al. [255]	Frequency band power	Advertisement like/dislike ratings	ANN	0.82	https://doi.org/10.1007/978–3-319–16,480-9_68
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	https://doi.org/10.1016/j.inffus.2016.09.003
Tyson-Carr et al. [262]	ERP and eye-movements	Product willingness to pay	Independent component analysis	N/A	https://doi.org/10.1016/j.neuroimage.2019.116213
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	https://doi.org/10.14569/IJACSA.2022.0130137
Wang et al. [287]	EEG and ET data	Self-reported preference	PCA, Random Lasso, SVM	0.80, 0.75,0.92	https://doi.org/10.1016/j.aei.2020.101095
Wei et al., [291]	EEF TF data	Advertisement effectiveness	SVM	0.75	https://doi.org/10.3389/fnins.2018.00076
Yadava et al. [294]	EEG TF effects	Like/dislike ratings of e-commerce products	HMM	0.68	https://doi.org/10.1007/s11042-017–4580-6
Yılmaz et al. [303]	Power spectral density	Product like/dislike ratings	Logistic regression	N/A	https://doi.org/10.1016/j.cmpb.2013.11.010
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	https://doi.org/10.18502/fbt.v7i3.4621
Zeng et al. [308]	Power spectral density, hemispheric asymmetry, differential entropy, Hjorth parameters	Sport shoes like/dislike ratings	KNN, SVM	0.94, 0.8	https://doi.org/10.3389/fnhum.2021.793952
Zheng et al. [313]	PSD	Emotion classification	DNN	0.85	https://doi.org/10.1109/TCYB.2018.2797176
Zhu et al. [314]	EEG and ET mixed measures	Self-reported preference	SVM, RF, CNN	70.26, 72.15, 96.4	https://doi.org/10.1016/j.aei.2022.101601