One-third of patients suffering from chronic epilepsy, which is caused by abnormal brain activity, is drug-resistant. Animal models are widely used to study the mechanisms leading to epilepsy so better drug treatments can be developed for this disease. In such studies, epileptiform activity, assessed by LFP recordings, can be used as a marker for the development and chronification of disease. However, the analysis of LFP recordings is typically done manually, which is time-consuming, subject to observer bias, error-prone, and lacks consistency and efficiency. Therefore, we present a work which developed a new, automated detection and classification method for epileptiform activity, which was tested in the intrahippocampal kainic acid (IHKA) mouse model, a model of human temporal lobe epilepsy. Our method relies on a spike detector using an improved version of the nonlinear energy operator (NEO) in combination with automatic NEO thresholding (ANT). The detected spikes form the basis of epileptiform event detection and classification. The proposed method is implemented in Python as an automated and time-efficient algorithm that can be used in preclinical studies. Epileptiform event detection accuracy was 93.1% and classification accuracy 95.8%. Moreover, the time for analysis of LFP recordings was reduced by 98.8% compared to manual analysis. Additionally, to demonstrate the potential of the algorithm for application in Brain-Machine Interfaces (BMI), we performed a real-time implementation using both an application-specific integrated circuit (ASIC) and a field programmable gate array (FPGA). The FPGA demonstrated the feasibility of real-time implementation, and the ASIC resulted in an area and power efficient chip using the Taiwan semiconductor manufacturing company (TSMC) 45nm library that constitutes a post-layout area of 9114 µm2 and a power usage of 6.11 µW.