Objective.　Electroencephalogram　(EEG)　analysis　has　always　been　an　important　tool　in　neural　engineering,　and　the　recognition　and　classification　of　human　emotions　are　one　of　the　important　tasks　in　neural　engineering.　EEG　data,　obtained　from　electrodes　placed　on　the　scalp,　represent　a　valuable　resource　of　information　for　brain　activity　analysis　and　emotion　recognition.　Feature　extraction　methods　have　shown　promising　results,　but　recent　trends　have　shifted　toward　end-to-end　methods　based　on　deep　learning.　However,　these　approaches　often　overlook　channel　representations,　and　their　complex　structures　pose　certain　challenges　to　model　fitting.　Approach.　To　address　these　challenges,　this　paper　proposes　a　hybrid　approach　named　FetchEEG　that　combines　feature　extraction　and　temporal-channel　joint　attention.　Leveraging　the　advantages　of　both　traditional　feature　extraction　and　deep　learning,　the　FetchEEG　adopts　a　multi-head　self-attention　mechanism　to　extract　representations　between　different　time　moments　and　channels　simultaneously.　The　joint　representations　are　then　concatenated　and　classified　using　fully-connected　layers　for　emotion　recognition.　The　performance　of　the　FetchEEG　is　verified　by　comparison　experiments　on　a　self-developed　dataset　and　two　public　datasets.　Main　results.　In　both　subject-dependent　and　subject-independent　experiments,　the　FetchEEG　demonstrates　better　performance　and　stronger　generalization　ability　than　the　state-of-the-art　methods　on　all　datasets.　Moreover,　the　performance　of　the　FetchEEG　is　analyzed　for　different　sliding　window　sizes　and　overlap　rates　in　the　feature　extraction　module.　The　sensitivity　of　emotion　recognition　is　investigated　for　three-　and　five-frequency-band　scenarios.　Significance.　FetchEEG　is　a　novel　hybrid　method　based　on　EEG　for　emotion　classification,　which　combines　EEG　feature　extraction　with　Transformer　neural　networks.　It　has　achieved　state-of-the-art　performance　on　both　self-developed　datasets　and　multiple　public　datasets,　with　significantly　higher　training　efficiency　compared　to　end-to-end　methods,　demonstrating　its　effectiveness　and　feasibility.