Acousto-electrophysiological neuroimaging (AENI) is a novel method for non-invasive and spatially selective recording of deep neuronal activity that leverages the interaction between focused ultrasound and brain tissue. When an ultrasound wave is applied to a target region, neurons within that region vibrate, producing periodic changes in their distance to recording electrodes such as invasive local field potential (LFP) or non-invasive transcranial electroencephalography (EEG) electrodes. This vibration induces a mixing between the neural signals and the ultrasound carrier, resulting in a frequency shift of the local activity. Therefore, signals from the targeted region produce sideband components at frequencies centred around the ultrasound carrier, while signals from outside the targeted regions remain unshifted. In this work, we propose a new frequency-domain decoding algorithm that selectively extracts the frequency shifted components, thereby isolating activity from the region of interest while suppressing interference from other brain regions. Simulations performed in NetPyNE at 500 kHz and 1 MHz ultrasound frequencies demonstrate that the proposed decoding method achieves up to 25 dB signal-to-interference ratio (SIR) improvement using subthreshold ultrasound pressures, which are too low to induce neuromodulation. These results establish acousto-electric frequency shifting and filtering of neuronal activity as an effective technique for enhancing spatial resolution in electrophysiological recordings, with potential applications in neuroscience research and clinical diagnostics.