Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification

Abstract

This work presents a method for classifying EEG (Electroencephalogram) signals generated when a person concentrates on specific words, defined as “Imagined Speech”. Imagined speech is essential to enhance problem-solving, memory, and language development. In addition, imagined speech is beneficial because of its applications in therapy fields like managing anxiety or improving communication skills. EEG measures the electrical activity of the brain. EEG signal classification is difficult as the machine learning (ML) algorithm has to learn how to categorize the signal linked to the imagined word. This work proposes a novel method to generate a specific feature vector to achieve classification with superior accuracy results to those found in the state of the art. The method leverages a genetic algorithm to create an optimal feature combination for the classification task and machine learning model. This algorithm can efficiently explore ample feature space and identify the most relevant features for the task. The proposed method achieved an accuracy of 96% using eight electrodes for EEG signal recordings.

1. Introduction

Imagined speech, also known as silent speech or inner speech, is thinking in the form of sound: “hearing” within one’s head silently, without the intentional movement of any limbs such as the lips, tongue, or hands [1].

Processing imagined speech is important because it helps us understand how the brain generates and controls inner thoughts, supports communication skills, and aids in developing brain–computer interfaces (BCI) [2].

Imagined speech improves cognitive processes such as memory by activating neural mechanisms associated with internal rehearsal and working memory. Studies suggest that subvocal articulation, whether expressed or imagined, facilitates encoding and retrieval by strengthening the neural circuits involved in language processing. This phenomenon is especially relevant in memory processes, where inner speech helps to organize and retain information [3].

Advances in this field may lead to more effective therapeutic strategies for individuals with speech disorders, as interventions can be designed to target specific neural pathways associated with particular aspects of speech. A speech or language disorder is characterized by difficulties forming or creating sounds crucial for effective communication. These disorders can manifest in various ways, the most common being articulatory, phonological, voice, and resonance disorders. Articulatory disorders involve difficulties physically producing certain sounds, often due to poor tongue, lips, or jaw positioning [4].

On the other hand, phonological disorders originate in the cognitive aspect of sound production. In these disorders, the person has difficulty understanding the sound rules of their language, leading to error patterns when speaking. Voice disorders affect speech quality, pitch, or volume, often due to problems with the vocal cords. In contrast, resonance disorders arise from abnormal airflow through the oral or nasal cavities, which affects sound clarity [5].

Worldwide, approximately 1% of the population has some form of speech, language, or communication difficulty, a condition often referred to as speech and language impairment (SLI). This statistic translates to millions of people experiencing barriers to expressing themselves or understanding others, impacting their social interactions, educational experience, and overall quality of life. The loss or impairment of these fundamental communication skills is widely considered a significant barrier limiting these individuals’ ability to interact and integrate into society [6].

Therefore, advances in this field may lead to more effective therapeutic strategies; furthermore, understanding these neural underpinnings lays the groundwork for developing new assistive technologies, such as BCIs, which could restore or enhance the communication abilities of individuals with severe speech impairments. Exploring these neurological mechanisms continues to be a vital area of research, promising to improve the lives of millions of individuals affected by speech and language disorders [7].

Imagined speech has been possible since the emergence of language; however, the phenomenon is primarily associated with its investigation through signal processing and detection within EEG data.

This task is difficult due to several interrelated factors: it demands significant cognitive resources, leading to potential overload; distractions from other thoughts can disrupt it; incorporating sensory experiences adds unnecessary noise; the lack of auditory and vocal feedback makes it hard to gauge coherence; coordinating various brain regions complicates the process; and individual differences in the vividness of inner speech further influence the ease of imagining speech. These elements create a challenging and often noisy cognitive environment for processing inner dialogue.

Due to the high complexity of EEG signals in imagined speech, classification is difficult. For this reason, signal databases are composed of simple, imagined vocal structures. The authors of [8,9,10] used a database with five vowels (a, e, i, o, u) in English, reporting a classification accuracy of 35.7%, 87%, and 79.7%, respectively. The work of [11] proposes four words, two of which are in English (Yes, No) and two in Hindi (Haan, Na), and reports 73.4%. The dataset in [12] uses two English words (Yes, No), and they achieved an accuracy of 63.16%.

The simpler the words are, the easier it is for the classifier model to classify them and, therefore, the greater their precision. Otherwise, the more complex the words, the more difficult the task and, therefore, the lower the precision.

Within the state-of-the-art, we observe a variety of features being used. For instance, ref. [9] utilizes 4 features (mean, variance, standard deviation, skewness), ref. [10] employs 32 features, and ref. [13] uses 44 features.

It is necessary to characterize the signals using the “Feature Extraction” technique in order to have information about them. It is hard to identify the changes in the signal directly when thinking about one word or another. Hence, the feature extraction technique needs to be implemented. Furthermore, depending on the nature of the signals and the classifier algorithm, the characteristics are different, so there is no standard for extracting the type and quantity of features. Traditional methods are based on digital signal processing. This is the case in ref. [12] who used Artificial Neural Networks (ANN) or [14] where the authors used Deep Neural Networks (DNN) to analyze the features extracted from the signal. Also, in [15], the authors use the Ant Colony Optimization (ACO) algorithm for the feature extraction of EEG signals.

This feature selection can be optimized through Genetic Algorithms (GA) as proposed in [16], where they used GA as a signal processing method for feature extraction. Another work is [17], where he uses GA for the optimal classification of epileptic seizures in EEG using wavelet analysis. Another example of this comes from [18], where they applied EEG electrode selection for person identification through a GA.

Furthermore, in the development of robust descriptors for specific environments, the authors of [19] proposed a modular framework that employs a GA to optimize descriptor configurations by balancing size, efficiency, and invariance to transformations such as lighting, scaling, and rotation. Their approach incorporates binary-coded genomes to control image-processing parameters and descriptor assembly, with modules selectively activated to achieve dimensionality reduction. By iteratively refining the descriptors, their framework demonstrated superior classification performance in sparse image regions compared to standard descriptors, while maintaining competitive invariance to environmental changes. This work highlights the potential of GA-driven optimization for scene-specific feature extraction.

Once the selection of features has been optimized, a classifier model is used. Among the classical machine learning (ML) models used in similar works, [20] used Support Vector Machines (SVM), while in [21], the authors applied Naive Bayes, and in [22], the results were calculated with Random Forest.

Other authors applied Deep Learning (DL) techniques. While [12] used Recurrent Neural Networks (RNN). Ref. [8] used a Convolutional Neural Network (CNN) in conjunction with Transfer Learning (TL), and finally, in [23], they used a combination of CNN + RNN.

The authors of [13] proposed a processing technique with 44 statistical characteristics extracted from EEG signals. This inspired the present work, which uses 214 features capable of characterizing the EEG signals from an imagined speech in greater depth. Subsequently, a GA was implemented to choose and load the features with the most significant information to the classifier model. State-of-the-art methodologies yield accuracies ranging from 35.7% to 87%, while our proposed approach demonstrates significant improvement, as shown in our results.

Figure 1 presents a bibliometric analysis of research trends in imagined speech, highlighting the integration of CNNs and SVMs as dominant classification models. Furthermore, it reveals a strong connection between feature extraction, signal processing, and classification, indicating a trend toward innovative techniques to improve imagined speech classification tasks.

The main contributions of the present work are as follows:

• The development of feature generation to extract information from the raw signals and their respective frequency bands.
• The implementation of GA to find the selection of electrodes that allow a more efficient classification.
• The implementation of GA to select the most relevant features that maximize classification accuracy.
• The comparison of ten different classification models.
• The dataset used was added to a public repository and will allow other techniques to be compared with this methodology.

Organization

The following sections describe the methodology used to classify imagined speech signals. The document is organized as follows: Section 2 contains the description of the dataset, the computational resources, preprocessing signals techniques, implementation of feature extraction, the optimization of GA, and the development and implementation of classifier models. Section 3 includes data classification analysis and discussion of evaluation metrics. Section 4 describes and interprets the article’s findings and future work on EEG systems for imagined speech classification tasks. Finally, Appendix A describes the equations employed for feature extraction in depth.

2. Methodology

The architecture of this work, Figure 2, filters the signals, uses feature extraction, and optimizes feature combinations using a GA to maximize the accuracy of a classifier model. These processes will be described in the following sections.

2.1. Dataset

For the project, the EEGIS (Electroencephalogram Imagined Speech) dataset [24] was used, where the inclusion criterion was that the participants were between the ages of 20 and 30. The participants had an average age of 24.8 years, with a standard deviation of 2.04. The signals were obtained using a 14-channel Emotiv Epoc+, as shown in Figure 3a, and these were recorded at 128 Hz. The channels are specified in Figure 3b.

A graphical interface was developed that randomly plays the words through speakers while the user imagines speaking the word for 8 s, during which the device records EEG signals. The subject was required to remain still with their eyes closed. Then, the recorded 8 s signals were divided into 1 s segments (128 frames each) with an overlap of 48 frames. The dataset contains 4044 instances for the nine classes (8 Spanish words and a blank state of mind). However, it was filtered only to use two simple words, /Sí/ and /No/, with 444 and 480 instances, respectively; therefore, there are 924 instances, so we have a tensor with the form (924, 14, 128). Figure 4 shows an example of this. A class balance was not performed since they are practically balanced. The data were divided into training and test sets with a proportion of 80–20%.

The rationale behind this ratio is for the following reasons: Focus on real-world performance: The 80–20 ratio is a widely accepted and well-known ratio used in many real-world machine learning classification tasks. This ratio allowed us to evaluate the model’s generalization capability on an established test set representative of typical use cases in production environments. Computational efficiency: Cross-validation can be computationally expensive and time-consuming, especially with a large dataset. Given the aforementioned preprocessing steps, in addition to the implementation of a GA (explained in depth later), it requires 100 GA individuals, 100 generations in the GA, 10 models to compare (particularly the kNN classifier model that searches across 7 k values), and 700,000 different models to be trained, resulting in a highly demanding process for the aforementioned setup. However, the 80–20% ratio provided us with a balance between sufficient model evaluation and reasonable training time.

2.2. Computational Resources

• CPU: Intel Core i5 11gen.
• GPU: Nvidia RTX 3060ti.
• Ram: 16 GB.
• vRam: 8 GB.
• OS: Windows 11.
• Libraries: numpy, pandas, scipy, sklearn, matplotlib, antropy, and signalityca.

2.3. Split Signals into Frequency Bands

A 4th-order band-pass Butterworth filter was used with cut-off frequencies (

Table 1. Cut-off frequencies for each band.

Band	Lower Cut-Off Frequency	Upper Cut-Off Frequency
Delta	0.5 Hz	4 Hz
Theta	4 Hz	8 Hz
Alpha	8 Hz	13 Hz
Beta	13 Hz	30 Hz
Gamma	30 Hz	40 Hz

) to separate each of the 14 channels into five frequency bands associated with brain rhythms (Figure 5). The shape of the data tensor (924, 128, 14) became 4D (924, 128, 14, 6), where six corresponds to the raw signals plus the five frequency bands.

2.4. Feature Extraction

Feature vectors of size 214 were constructed, with each vector comprising 36 features extracted from six different layers of the same signal: the original signal and five frequency bands. Specifically, the first 34 features were derived from the raw unit signals, while 36 were from each frequency band. Altogether, these features add up to 214, forming the feature maps. The features were extracted from the 14 channels; therefore, the shape of the feature maps was (14, 214), as shown in Figure 6.

The features employed are the following:

• Mean [25];
• Standard deviation [26];
• Coefficient variation [27];
• Median [26];
• Mode [26];
• Max [28];
• Min [28];
• First quartile [29];
• Third quartile [29];
• Interquartile range [29];
• Kurtosis [26];
• Skewness [26];
• Detrended fluctuation analysis [30];
• Activity Hjorth param [31];
• Mobility Hjorth param [31];
• Complexity Hjorth param [31];
• Permutation entropy [32];
• Approximate entropy [33];
• Spectral entropy [34];
• Higuchi fractal dimension [35];
• Total power spectral density [36];
• Centroid power spectral density [37];
• Determinism [38];
• Trapping time [38];
• Diagonal line entropy [38];
• Average diagonal line length [38];
• Recurrence rate [38];
• Spectral edge frequency 25 [39];
• Spectral edge frequency 50 [39];
• Spectral edge frequency 75 [39];
• Hurst exponent [40];
• Singular valued decomposition entropy [41];
• Petrosian fractal dimension [42];
• Katz fractal dimension [43];
• Relative band power [44];
• Band amplitude [45].

These 36 features are described in depth in the Appendix A.

2.5. Optimization with Genetic Algorithm

We seek a combination of features to maximize the machine learning model’s accuracy. Because the number of possible combinations is enormous, the best choice is to use a GA, a metaheuristic that will optimize the selection of the optimal features. The GA will generate a population of genomes to reproduce and evolve them through generations. In this case, the genomes will be 228-length binary vectors, where the first 14 elements indicate which electrodes will be activated, and the remaining 214 elements indicate which features must be activated to improve. The selection of the features will be optimized using a classic machine learning classification model, where the model’s accuracy (2) is the objective function, converting this into a maximization problem. The parameters of the GA are as follows:

• Number of individuals: 100;
• Type of gen: dichotomic {0,1};
• Number of genes: 228;
• Type of selection: rank;
• Type of crossover: homogenous;
• Mutation: bit flip;
• Fitness function: accuracy.

2.5.1. Explanation of the GA and Its Inputs

As mentioned above, a Genetic Algorithm (GA) is applied to optimize the selection of electrodes and features to maximize classification accuracy. A GA is a metaheuristic search algorithm inspired by Darwinian evolution. It efficiently finds an approximate optimal solution within a vast search space, which, in this case, consists of 228 (factorial) possible combinations of electrodes and features, which is too many for an exhaustive computational search.

2.5.2. Individuals in the GA: Not the Subjects

It is important to clarify that the term “individuals” in the context of the GA does not refer to the 10 male subjects in our dataset. In contrast, in GA, each individual represents a possible solution; in this case, a binary vector of length 228 encodes which electrodes and features are selected (1) or not (0).

2.5.3. GA Works as Follows

• Initialization:

  -

  A population of 100 binary vectors is randomly generated (a common population size in GA).

• Selection and Reproduction:

  -

  These 100 individuals form pairs and recombine (cross over) to create offspring.

  -

  Since each pair produces two offspring, the population temporarily doubles to 200 individuals.

• Evaluation and Survival of the Fittest:

  -

  Each individual (binary vector) defines a specific subset of electrodes and features used to train and evaluate a classification model.

  -

  Individuals are then ranked based on the model’s accuracy.

  -

  The 100 best individuals survive to the next generation, while the 100 worst are discarded.

• Iterative Optimization:

  -

  This process is repeated over several generations, refining the selection of electrodes and features until the algorithm converges on an optimal subset.

• Final Solution Selection:

  -

  In the last generation, the highest-ranked individual (binary vector) is selected as the final solution.

  -

  This vector indicates, with 0 and 1, which electrodes and features provide the most valuable information to the model.

Using GA, we efficiently navigate the ample search space and identify the most relevant features and electrodes without requiring exhaustive calculations.

Upon reaching the end criterion, the GA returns the best genomes (binary arrays) and the fitness obtained (the accuracy obtained with those feature activations of the binary arrays).

2.6. Data Standardization

The data must be processed, normalized, or standardized before entering the machine learning model to obtain better performance. The proposed model uses standardization (Equation (1)).

$$z={\displaystyle \frac{x_i-\mu }{\sigma }}$$

(1)

where $x_i$ is the data point to standardize, $\mu$ is the mean of all the data points in the column, and $\sigma$ is the standard deviation of the data in the column. It is necessary to have all the data attributes in a standard scale without distorting differences in the ranges of values or losing information.

2.7. Flatten Data

As mentioned, the feature maps have the shape (14, 214). However, the GA selects the best features. Therefore, the matrices now have the shape of (M, N) where M is the number of electrodes, and N is the number of features the genome indicates. Since a classic ML model needs a data vector as input, the data point must be converted into the correct shape, flattening the feature matrices of (M, N) to (1, M × N) feature vectors, as shown in Figure 7.

2.8. Classifier Model

The problem to be solved lies in the classification of EEG signals with an AI algorithm that knows how to discern when an EEG signal is associated with the thought of a particular word. The proposed method uses a classifier model to identify (predict) the imagined word by recognizing patterns and characteristics in the EEG signal. Separating databases into training and testing sets is a common step when working with classifier models. With the training set, the model recognizes, learns, and adjusts to the patterns of the given signals. On the other hand, the testing set contains examples of new signals for the model since it has not seen them. This set validates the accuracy of the model and its classification capacity. As mentioned at the beginning, this work seeks to demonstrate that a classic machine learning algorithm can classify imagined speech. For this reason, 10 different classifier models were compared:

• k-Nearest Neighbors (kNN);
• Logistic Regression (LogReg);
• Random Forest (RF);
• Decision Tree (DT);
• Gradient Boosting Machine (GBM);
• AdaBoost;
• Naïve Bayes (NB);
• Linear Discriminant Analysis (LDA);
• Multi-Layer Perceptron (MLP);
• Support Vector Machine (SVM).

For each classifier model, the default parameters were used. However, for kNN, a heuristic is applied to each individual in every GA generation to search for the optimal value of k from {5,7,13,15,17,21,37} that maximizes ACC. The final model’s performance was evaluated using the metrics accuracy, precision, recall, and F1-score, which can be found in (2)–(5), respectively.

$$\mathrm{accuracy}={\displaystyle \frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}}}$$

(2)

$$\mathrm{precision}={\displaystyle \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{TN}}}$$

(3)

$$\mathrm{recall}={\displaystyle \frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}}$$

(4)

$$\mathrm{F}1\text{-}\mathrm{score}={\displaystyle \frac{2·\mathrm{precision}·\mathrm{recall}}{\mathrm{precision}+\mathrm{recall}}}$$

(5)

where the elements of (2)–(5) are as follows: TP (true positives), TN (true negatives), FP (false positives), and FN (false negatives).

3. Results

We can observe the GA’s performance for the ten classifier models in Figure 8. In particular, we can observe the performance of the kNN model, Figure 8a, where it started with an average accuracy below 70%; despite this, the population evolved, achieving an accuracy above 96% after 20 generations.

The GA’s output was the best genome, i.e., a binary vector of size 228, where the positions containing 1s indicate that the electrode or the feature associated with that position was activated. The best solution found by the GA was as follows: best genome = [1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1]. As the Feature Extraction section mentions, the first 34 elements correspond to features extracted from the original signal. The last 180 elements are five groups of 36 features. Each group corresponds to features in the delta, theta, alpha, beta, and gamma bands, respectively. Also, the algorithm returns the best k value for the k-NN algorithm. In this case, k = 7 was the optimal value.

In addition, when the genome was decodified, it showed the selection of electrodes found by the GA. In total, eight electrodes provided more information to the model. These were as follows: AF3, T7, O1, O2, P8, T8, F4 and F8, as shown in Figure 9.

Furthermore, the decodified features are the ones shown in

Table 2. Feature activation found by the GA for the kNN classifier.

Feature	Raw Signal	Delta	Theta	Alpha	Beta	Gamma
Mean		X			X	X
Standard deviation	X	X		X
Coefficient variation	X		X
Median	X	X			X
Mode	X	X		X
Max		X	X	X		X
Min			X			X
First quartile		X			X
Third quartile	X				X
Interquartile range		X	X	X	X
Kurtosis
Skewness	X	X	X	X	X
Detrended fluctuation analysis		X	X	X		X
Activity Hjorth param	X	X	X		X
Mobility Hjorth param		X	X		X
Complexity Hjorth param	X		X	X	X
Permutation entropy	X	X	X	X	X	X
Approximate entropy	X	X		X
Spectral entropy		X	X		X
Higuchi fractal dimension		X		X
Total power spectral density	X		X			X
Centroid power spectral density	X	X			X	X
Determinism						X
Trapping time		X				X
Diagonal line entropy		X		X	X	X
Average diagonal line length		X			X
Recurrence rate					X	X
Spectral edge frequency 25						X
Spectral edge frequency 50	X	X			X	X
Spectral edge frequency 75	X		X	X	X
Hurst exponent					X
Singular valued decomposition entropy		X				X
Petrosian fractal dimension		X	X
Katz fractal dimension	X		X	X
Relative band power					X
Band amplitude		X	X	X	X	X
Total features activated	15	23	14	16	20	15

.

The Genetic Algorithm’s outputs were validated in a separate experiment. The data were split into train and test sets with a meticulous 20–80 proportion, ensuring a thorough and reliable validation process.

The features used were the ones indicated by the GA, such as the k = 9 value for the k-NN classifier. The confusion matrices of the classifier models can be observed in Figure 10.

The metrics specified in Equations (2)–(5) were calculated from the confusion matrices of each of the models, and these can be seen in

Table 3. Model’s performance metrics.

Classfier	Electrodes (Max: 14)	Features (Max: 214)	Recall	F1-Score	Precision	Accuracy
kNN	8	103	0.97	0.94	0.95	0.96
LogReg	5	107	0.87	0.85	0.85	0.87
RF	7	106	0.75	0.76	0.84	0.80
Decision Tree	8	101	0.82	0.81	0.81	0.82
GBM	6	99	0.81	0.82	0.87	0.84
AdaBoost	5	107	0.80	0.77	0.78	0.80
Naïve Bayes	7	101	0.87	0.81	0.82	0.84
LDA	9	106	0.88	0.84	0.85	0.87
MLP	9	112	0.78	0.78	0.79	0.80
SVM	5	110	0.89	0.76	0.78	0.82

.

In addition, in Table 3, we can observe that the classification performance varied across different models with notable differences in recall, precision, F1-score, and accuracy. The k-Nearest Neighbors (kNN) classifier model showed the highest overall performance, achieving a recall of 0.97, an F1-score of 0.94, and an accuracy of 0.96, using eight electrodes and 103 features, which is expected since it was the only model that included a heuristic search within the GA to find the best k parameter. In contrast, models such as Random Forest (RF) and Multi-Layer Perceptron (MLP) exhibited lower recall values of 0.75 and 0.78, respectively, with corresponding accuracy scores of 0.80. Despite using only five electrodes, the Support Vector Machine (SVM) achieved a recall of 0.89, although its F1-score remained at 0.76. Similarly, Logistic Regression (LogReg) and Linear Discriminant Analysis (LDA) showed balanced performances with accuracy scores of 0.87 using five and nine electrodes, respectively. The Gradient Boosting Machine (GBM), with six electrodes and 99 features, showed moderate performance, with a recall of 0.81 and an F1-score of 0.82. These results clearly show the relationship between the number of electrodes used and classification effectiveness, where some models achieved high accuracy with fewer electrodes. In contrast, others required a higher number of features to optimize performance.

Table 4. Comparing this work with related works in the state-of-the-start.

Article	Dataset (Words)	Processing	Classifier	Accuracy
Our work	/Sí/, /No/	Feature Extraction, Genetic Algorithm	kNN	96%
[8]	/a/,/e/,/i/,/o/,/u/	ICA	CNN+TL	35.7%
[9]	/a/,/e/,/i/,/o/,/u/	Statistical characteristics	ELM, LDA, SVM	87%
[10]	/a/,/e/,/i/,/o/,/u/	Statistical characteristics	SVM, DT, LDA, QDA, PCA	79.7%
[11]	/Yes/,/No/,/Haan/,/Na/	Digital filters	ANN, SVM, RF, ADA	73.4%
[12]	/Yes/,/No/	ICA, digital filters and DWT	LDA, SVM, kNN, NB, MLP	63.16%

presents the differences between similar works in the state-of-the-art, such as the types of words utilized, the processing techniques, and the classifier models. Also, in the last column, we can spot the performance of each methodology, indicating where our technique achieved the highest accuracy.

4. Discussion

The proposed method demonstrates excellent performance in recognizing and distinguishing the simple Spanish words /Sí/ (Yes) and /No/ (No). This work achieved higher accuracy through rigorous testing than existing methods in the current literature on the state of the art. The proposed approach effectively handles fundamental words and outperforms previous techniques in terms of precision. This achievement highlights the method’s robustness and efficiency, showcasing its potential for applications that require accurate recognition of these essential yet crucial responses in Spanish, making it a significant contribution to the field. Moreover, in future work, we seek to use the full EEGIS dataset containing complex words of the language and test more classifier models of both ML and DL that surpass this work.

Although the proposed approach effectively handles core words and outperforms previous techniques in terms of accuracy, it is important to emphasize this work’s limitations.

First, the limited vocabulary size is a key limitation. While our model shows promising results within the predefined word set, it does not yet scale to the full complexity of natural language. Expanding the vocabulary requires more data collection and model tuning, which is an important direction for future research.

Second, inference time represents a challenge for real-time implementation. The processing flow (feature extraction and classification) takes a few seconds per instance, even when running on a research computer. That introduces latency that can disrupt the fluidity of real-time interaction. Optimizing the algorithm’s computational efficiency, leveraging hardware acceleration (e.g., GPUs or dedicated edge computing devices), and exploring lightweight model architectures could help to mitigate this issue in future iterations.

Finally, the headset configuration presents practical limitations. The electrodes, which use a saline solution, are prone to evaporation over time, potentially degrading signal quality. Furthermore, prolonged use can cause skin irritation at the contact points. To address these issues, alternative electrode materials can be explored, headset ergonomics can be improved, or automated hydration mechanisms can be incorporated to maintain signal stability over extended periods.

Despite these limitations, our study lays the groundwork for future advancements. Implementing this system in a real-time solution would require the further optimization of computational efficiency, robustness in signal acquisition, and user comfort. These challenges will be the focus of our future work as we move toward practical, real-world applications.