Transcranial acoustoelectric brain imaging (tABI) is a potential neuroimaging technique with high spatial (~mm) and temporal (~ms) resolution. Decoding precision is critical for decoded signal quality and imaging performance. However, traditional method is based on the fundamental component of acoustoelectric (AE) signal, overlooking the valuable information of its harmonics. Here we present a signal decoding framework combining fundamental and harmonica components. Adopting filter bank analysis to efficiently integrate the fundamental and harmonic components, a weighted filter bank Hilbert transform (wFBHT) decoding method is implemented. With steady-state visual stimulation, both brain electric signal (steady-state visual evoked potential ， SSVEP) and AE signal of live rats were recorded using tABI. First, the signal-to-noises (SNRs) characteristics of harmonic was analyzed. The decoded AE (decAE) signals of 2nd-6th harmonic exhibited highly consistent spectral response with SSVEP, which provided theoretical basis for the framework. Then, using the grid search and nonlinear fitting, the impacts of harmonic number N and weight w on the change rate of correlation coefficient (CRCC) between decAE signal and SSVEP were further explored. Results demonstrated that CRCC increased with harmonic number N significantly superior to traditional method (p=0.0004). With equal weight of 1st-4th harmonics, the highest correlation coefficient increased by 47%. What’s more, the feasibility and performance were validated in live rats. Compared with that of traditional method, the SNR of decAE signal with wFBHT was 20.20dB and increased by 72.1%.