When it comes to basic brainwave recognition, one of the most recommended EEG devices is the Emotiv EPOC+. This advanced headset is equipped with 14 electrodes that are strategically placed to capture brain activity accurately. The Emotiv EPOC+ offers a user-friendly interface and software that allows for seamless data analysis and visualization.
For example, if the frequency of the targeted stimulus is 15 Hz, the frequency of the generated SSVEP will also be 15 Hz. Therefore, the user pays attention visually to a target, and the BCI determines the target through analyzing the SSVEP features. The recent developments on human-computer interaction (HCI) that allows the computer to recognize the emotional state of the user provide an integrated interaction between human and computers. This platform propels the technology forward and creates vast opportunities for applications to be applied in many different fields such as education, healthcare, and military applications [131].
Key Features of the Emotiv EPOC+
Depending on the type of recursive least squares algorithm, its convergence may be faster than that of the LMS algorithm, but its computational cost is greater. A disadvantage of using adaptive filtering is that providing reference input requires more sensors [64]. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a standard systematic review and meta-analysis guideline [38], was used in this study. An important component of this systematic review involved the clear definition of research questions to reduce the effects of research expectations. Furthermore, our research method followed the Cochrane Collaboration definitions [39] to minimize the risk of bias.
Figure 2a shows a sample CFM constructed with PCC, which indicates the correlation between signals collected from two EEG channels. Similarly, phase synchronization, dependency, and causal relationship between two signals are indicated by Fig. 2 that the elements of a matrix at position (left(X,Yright)) and (left(Y,Xright)) are the same (i.e., CFMs are symmetric) for functional connectivity methods PCC, PLV, and MI. However, these are not the same (i.e., asymmetric CFM) for effective connectivity method TE.
In this case, the LR algorithm outperformed both SVM and KNN, with overall accuracy rates ranging from 37% to 90%. Behri et al. [104] have compared several algorithms that might be used to differentiate EEG signals from the right foot and the right hand in five study participants. WPD was used to extract the features, and the RF classifier and WPD achieved a maximum accuracy of 98.45% in all participants. Similarly, Attallah et al. [259] have applied four levels of WPD to decompose EEG signals.
The Emotiv EPOC+ is known for its high-quality signal resolution, making it ideal for basic brainwave recognition tasks. Additionally, this EEG device offers real-time feedback, which is essential for monitoring brain activity during various activities or experiments. With its wireless connectivity, users can move around freely without being restricted by cords or cables.
When we want to know the activity of the cardiac autonomic nervous system corresponding to different emotional states, we can use the ECG to complete it (Agrafioti et al., 2011). For example, the frequency or depth of breathing is closely related to emotional changes (Zhang et al., 2017). However, during the experiment, the spontaneous physiological signals in the case will encounter problems such as no quantification standard and low classification accuracy when quantifying emotions (Fairclough, 2009; Nie et al., 2011). In processing automatic signals, EEG signals can provide a direct and comprehensive method for emotion recognition (Mauss and Robinson, 2009; Waugh et al., 2015). The performance of the model is compared with SVM [31], BiDANN [32], and BiHDM [33]. SVM is a classical machine learning method, while the other methods are more advanced.
Benefits of Using the Emotiv EPOC+
Moreover, more attention should be given to removing artifacts from EMG, EOG, and ECG. In an EEG-based authentication system, different paradigms like P300, SSVEP, and MI can be used, although each paradigm has its own merits and demerits. Thus, the best paradigm for EEG should be identified based on the person’s authentication.
One of the main benefits of using the Emotiv EPOC+ for basic brainwave recognition is its affordability compared to other EEG devices on the market. Despite its lower price point, the Emotiv EPOC+ does not compromise on performance or accuracy. Additionally, its ease of use and portability make it a popular choice among researchers, students, and hobbyists alike.
In conclusion, the Emotiv EPOC+ is highly recommended for basic brainwave recognition due to its advanced features, user-friendly interface, and affordability. Whether you are conducting research, studying brainwave patterns, or simply curious about your own brain activity, the Emotiv EPOC+ is a reliable and efficient EEG device that delivers accurate results.
