Abstract
Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.
Highlights
Brain computer interfaces (BCI) are an important application of electrocephalogram (EEG) signals (Navalyal and Gavas, 2014)
This paper aims at studying the usage of optimization from the view point of the application in BCI, i.e., with respect to the standard BCI pipelines
This study summarizes the BCI applications that have used optimization and the parameters of BCI are reviewed in detail
Summary
Brain computer interfaces (BCI) are an important application of electrocephalogram (EEG) signals (Navalyal and Gavas, 2014). BCI system’s pipeline consists of the following blocks: pre-processing of the EEG data, event-related potential (ERP) analysis, extraction of features, and classification of data (Sinha et al, 2015b), and so on The effectiveness of these blocks can be measured as a function of time complexity, computational resources required, and the accuracy of the algorithms. Invasive EEG can aid in this regard but cannot be applied in day-to-day scenarios for all the participants In such cases, the domain knowledge can be of great help but in the lack of this knowledge for novel BCI systems, arriving at proper tuning parameters of BCI is very difficult.
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