Concerning the problem that the existing text-oriented ElectroEncephaloGraphy （EEG） decoding methods focus on global modeling while neglecting inter-channel local correlations and failing to align EEG and text representations precisely， an EEG decoding model based on dual-branch representation fusion and cross-modal alignment was proposed. In the model， a parallel dual-branch architecture was adopted to obtain high-quality representations of EEG signals： in the spatio-temporal branch， signal short-term dependencies， neighboring-channel spatial correlations， and cross-step long-range relationships were captured layer by layer through Bidirectional Long Short-Term Memory （Bi-LSTM） network， depthwise separable convolution， and gated axial self-attention mechanism； in the context fusion branch， based on a multi-layer Transformer encoder， cross-attention was used to fuse the two representations for complementary integration. Besides， to reduce semantic discrepancies between modalities， a joint loss combining triplet loss and covariance alignment was introduced， thereby constraining vector alignment by the geometric distance and second-order statistical properties between paired EEG and text representations. Experimental results on the ZuCo dataset show that the proposed model improves BLEU-1 （BiLingual Evaluation Understudy-1） by about 1.16 percentage points compared with the baseline EEG2Text， demonstrating its effectiveness in text-oriented EEG decoding tasks.