Challenges Exist in Translating Brain Signals Into Words Using Brain-Computer Interfaces (BCIs)

2. Methodology

The Brain to Word BCI research follows the following steps in its methodology:

People who meet the inclusion criteria will be approached for the study. Every participant will be asked to give their informed consent, and during the entire study, their privacy and confidentiality will be respected.

EEG Data Acquisition: To obtain EEG data, a hybrid EEG-BCI system comprising of BCI apparatus and EEG electrodes will be employed. Participants will wear the EEG headgear as they do a range of cognitive tasks and have their brain activity monitored. Data pre-processing: The acquired EEG data will be pre-processed using the appropriate signal-processing techniques to remove artifacts, filter the data, and extract relevant properties as shown in Figure 1.

Figure 1.

Process of translating neural signals into words

EEG Data Analysis: Machine learning techniques will be applied to the pre-processed EEG data to recognize the different cognitive states and extract the intended words from the participant's brain activity. Word Recognition and Communication: The extracted words will be turned into text and displayed on a screen via a text-to-speech or natural language processing system as shown in Figure 2.

Figure 2.

Transforming brainwaves into words

Evaluation of Performance: Recall, accuracy, precision, and F1-score are just a few of the performance metrics that will be used to gauge how accurate the word recognition system is.

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  Statistical Analysis: To ascertain the critical factors impacting the word recognition system's efficacy and to investigate the relationships between the retrieved words and the cognitive states, statistical analysis will be performed.

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  Ethical Considerations: A few of the ethical concerns that will be considered during the study are informed consent, data security, and participant privacy.