While Neuro-GPT offers significant advantages in handling EEG data scarcity and heterogeneity, there are potential limitations and biases to consider when utilizing such foundation models. One limitation is the risk of overfitting when fine-tuning on smaller datasets after pre-training on extensive EEG data. The model may struggle to generalize well if the downstream task differs significantly from the pre-training objectives. Biases might arise if the initial dataset used for pre-training is not diverse enough or contains inherent biases present in the collected EEG recordings. These biases could impact decision-making processes based on model predictions.