A channel selection method to find the role of the amygdala in emotion recognition avoiding conflict learning in EEG signals

Almanza-Conejo, Oscar; Avina-Cervantes, Juan Gabriel; Garcia-Perez, Arturo; Ibarra-Manzano, Mario Alberto

doi:10.1016/j.engappai.2023.106971

Cited by 4 publications

(1 citation statement)

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“…Moreover, the extensive number of EEG channels compounds the difficulties in data collection and significantly increases the computational complexity of processing this data, leading to increased and often unnecessary computational costs. Therefore, selecting appropriate channels (known as channel selection optimization) from the entirety has emerged as a pivotal challenge in the realm of BCIs (Almanza-Conejo et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs

Liu,

Wu,

et al. 2024

Front. Hum. Neurosci.

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BackgroundChannel 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.MethodsIn 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.ResultsThe 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.ConclusionThe 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.

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Section: Introductionmentioning

confidence: 99%