{Reference Type}: Journal Article
{Title}: Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.
{Author}: Liu T;Wu Y;Ye A;Cao L;Cao Y;
{Journal}: Front Hum Neurosci
{Volume}: 18
{Issue}: 0
{Year}: 2024
{Factor}: 3.473
{DOI}: 10.3389/fnhum.2024.1400077
{Abstract}: <B>UNASSIGNED: </B>Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems.<BR><B>UNASSIGNED: </B>In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.<BR><B>UNASSIGNED: </B>The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.<BR><B>UNASSIGNED: </B>The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.