Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typically require a large set of fully-sampled MR images for training, which can be difficult to obtain. In this paper, we present a novel fully self-supervised method based on implicit neural representation, which requires only a single under-sampled MRI instance for training. To effectively guide the self-supervised learning process, we introduced multiple novel supervisory signals in both the image and frequency domains. Experimental results indicate that the proposed method outperforms existing self-supervised methods and even a supervised method, demonstrating its strong reliability and flexibility.