Motor impairments caused by neurological diseases have an important impact on gait, particularly on the coordination between left and right lower limbs. Deviation from normal gait is often measured to assess this impact on gross motor functions, and to monitor the progress of patients during rehabilitation. The concept of gait dissimilarity map is introduced to represent bilateral raw gait signals, while accounting for their respective spatiotemporal dynamics. A model of gait for the healthy population is constructed through Singular Value Decomposition, considering both lower limbs. The obtained eigenvectors synthesize the symmetry present in gait. Then, by projecting the dissimilarity maps of patients with gait disorders on the space formed by such eigenvectors, we compute their associated Eigen-Gait Asymmetry Index (EGAI) relatively to an average normal gait reference vector. For the knee joint in the sagittal plane, EGAI values of patients are higher (9.73 ±2.16) than those of healthy controls (3.86 ±0.9), reflecting the asymmetry induced by neurological diseases. Patients with hemiparesis show the highest EGAI (10.4 ±1.8), followed by patients with paraparesis (9.9 ±1.8) and patients with tetraparesis (8.6 ±2.5). Indeed, patients with hemiparesis show a more asymmetrical gait since only one side of the body is affected. EGAI for hip, ankle and pelvis joints in the sagittal plane show similar trends. Our innovative method characterizes bilateral gait, enriching traditional unilateral assessments. Our method yields a comprehensive score reflecting both asymmetry and gait deviations, aiming to provide clinicians with an effective and precise monitoring tool.